forked from watml/CS886
-
Notifications
You must be signed in to change notification settings - Fork 0
/
causal.bib
915 lines (914 loc) · 39 KB
/
causal.bib
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
695
696
697
698
699
700
701
702
703
704
705
706
707
708
709
710
711
712
713
714
715
716
717
718
719
720
721
722
723
724
725
726
727
728
729
730
731
732
733
734
735
736
737
738
739
740
741
742
743
744
745
746
747
748
749
750
751
752
753
754
755
756
757
758
759
760
761
762
763
764
765
766
767
768
769
770
771
772
773
774
775
776
777
778
779
780
781
782
783
784
785
786
787
788
789
790
791
792
793
794
795
796
797
798
799
800
801
802
803
804
805
806
807
808
809
810
811
812
813
814
815
816
817
818
819
820
821
822
823
824
825
826
827
828
829
830
831
832
833
834
835
836
837
838
839
840
841
842
843
844
845
846
847
848
849
850
851
852
853
854
855
856
857
858
859
860
861
862
863
864
865
866
867
868
869
870
871
872
873
874
875
876
877
878
879
880
881
882
883
884
885
886
887
888
889
890
891
892
893
894
895
896
897
898
899
900
901
902
903
904
905
906
907
908
909
910
911
912
913
914
915
@inproceedings{AcharyaBDK18,
title = {Learning and Testing Causal Models with Interventions},
author = {Acharya, Jayadev and Bhattacharyya, Arnab and Daskalakis, Constantinos and Kandasamy, Saravanan},
booktitle = {Advances in Neural Information Processing Systems 31},
pages = {9447--9460},
year = {2018},
url = {http://papers.nips.cc/paper/8155-learning-and-testing-causal-models-with-interventions.html},
}
@article{AngristIR96,
title = {Identification of Causal Effects Using Instrumental Variables (with discussion)},
author = {Joshua D. Angrist and Guido W. Imbens and Donald B. Rubin},
journal = {Journal of the American Statistical Association},
volume = {91},
number = {434},
pages = {444--455},
year = {1996},
url = {https://www.tandfonline.com/doi/abs/10.1080/01621459.1996.10476902},
}
@inproceedings{AlaaSchaar19,
title = {Validating Causal Inference Models via Influence Functions},
author = {Alaa, Ahmed and {van} Der Schaar, Mihaela},
booktitle = {Proceedings of the 36th International Conference on Machine Learning},
pages = {191--201},
year = {2019},
url = {http://proceedings.mlr.press/v97/alaa19a.html},
}
@inproceedings{AndrewsSC20,
title = {On the Completeness of Causal Discovery in the Presence of Latent Confounding with Tiered Background Knowledge},
author = {Andrews, Bryan and Spirtes, Peter and Cooper, Gregory F.},
booktitle = {Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics},
pages = {4002--4011},
year = {2020},
url = {http://proceedings.mlr.press/v108/andrews20a.html},
}
@article{BackhoffBLZ17,
title = {Causal Transport in Discrete Time and Applications},
author = {Backhoff, Julio and Beiglb{\"o}ck, Mathias and Lin, Yiqing and Zalashko, Anastasiia},
journal = {{SIAM} Journal on Optimization},
volume = {27},
number = {4},
pages = {2528--2562},
year = {2017},
url = {https://doi.org/10.1137/16M1080197},
keywords = {journal},
}
@article {BareinboimPearl16,
title = {Causal inference and the data-fusion problem},
author = {Bareinboim, Elias and Pearl, Judea},
journal = {Proceedings of the National Academy of Sciences},
volume = {113},
number = {27},
pages = {7345--7352},
year = {2016},
url = {https://www.pnas.org/content/113/27/7345},
}
@inproceedings{BesserveSSJ18,
title = {Group invariance principles for causal generative models},
author = {Michel Besserve and Naji Shajarisales and Bernhard Sch{\"o}lkopf and Dominik Janzing},
booktitle = {Proceedings of the Twenty-First International Conference on Artificial Intelligence and Statistics},
pages = {557--565},
year = {2018},
url = {http://proceedings.mlr.press/v84/besserve18a.html},
}
@inproceedings{BesserveMSS20,
title = {Counterfactuals uncover the modular structure of deep generative models},
author = {Michel Besserve and Arash Mehrjou and R{\'e}my Sun and Bernhard Sch{\"o}lkopf},
booktitle = {International Conference on Learning Representation},
year = {2020},
url = {https://iclr.cc/virtual_2020/poster_SJxDDpEKvH.html},
}
@article{BottouPCCCPRSS13,
author = {L{\'e}on Bottou and Jonas Peters and Joaquin Qui{\~n}onero-Candela and Denis X. Charles and D. Max Chickering and Elon Portugaly and Dipankar Ray and Patrice Simard and Ed Snelson},
title = {Counterfactual Reasoning and Learning Systems: The Example of Computational Advertising},
journal = {Journal of Machine Learning Research},
year = {2013},
volume = {14},
number = {65},
pages = {3207--3260},
url = {http://jmlr.org/papers/v14/bottou13a.html},
}
@inproceedings{CelisMV20,
title = {Interventions for Ranking in the Presence of Implicit Bias},
author = {Celis, L. Elisa and Mehrotra, Anay and Vishnoi, Nisheeth K.},
booktitle = {Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency},
pages = {369–-380},
year = {2020},
url = {https://doi.org/10.1145/3351095.3372858},
}
@article{ChernozhukovFM13,
title = {Inference on Counterfactual Distributions},
author = {Chernozhukov, Victor and Fern{\'a}ndez-Val, Iv{\'a}n and Melly, Blaise},
journal = {Econometrica},
volume = {81},
number = {6},
pages = {2205--2268},
year = {2013},
url = {https://onlinelibrary.wiley.com/doi/abs/10.3982/ECTA10582},
}
@inproceedings{CreagerMPZ20,
title = {Causal Modeling for Fairness In Dynamical Systems},
author = {Elliot Creager and David Madras and Toniann Pitassi and Richard Zemel},
booktitle = {Proceedings of the 37th International Conference on Machine Learning},
year = {2020},
url = {https://icml.cc/virtual/2020/poster/6548},
}
@inproceedings{DAmour19,
title = {On Multi-Cause Approaches to Causal Inference with Unobserved Counfounding: Two Cautionary Failure Cases and A Promising Alternative},
author = {D'Amour, Alexander},
booktitle = {Proceedings of the 36th International Conference on Machine Learning},
pages = {3478--3486},
year = {2019},
url = {http://proceedings.mlr.press/v89/d-amour19a.html},
}
@article{Dawid79,
title = {Conditional Independence in Statistical Theory},
author = {A. Philip Dawid},
journal = {Journal of the Royal Statistical Society. Series B (Methodological)},
volume = {41},
number = {1},
pages = {1--31},
year = {1979},
url = {http://www.jstor.org/stable/2984718},
}
@article{Dawid80,
title = {Conditional Independence for Statistical Operations},
author = {A. Philip Dawid},
journal = {The Annals of Statistics},
volume = {8},
number = {3},
pages = {598--617},
year = {1980},
url = {http://www.jstor.org/stable/2240595},
}
@article{Dawid00,
title = {Causal Inference Without Counterfactuals (with discussion)},
author = {A. Philip Dawid},
journal = {Journal of the American Statistical Association},
volume = {95},
number = {450},
pages = {407--424},
year = {2000},
url = {http://www.jstor.org/stable/2669377},
}
@article{Dawid15,
title = {Statistical Causality from a Decision-Theoretic Perspective},
author = {Dawid, A. Philip},
journal = {Annual Review of Statistics and Its Application},
volume = {2},
number = {1},
pages = {273--303},
year = {2015},
url = {https://doi.org/10.1146/annurev-statistics-010814-020105},
keywords = {review},
}
@inproceedings{DingGZT19,
title = {Likelihood-Free Overcomplete ICA and Applications In Causal Discovery},
author = {Ding, Chenwei and Gong, Mingming and Zhang, Kun and Tao, Dacheng},
booktitle = {Advances in Neural Information Processing Systems 32},
pages = {6883--6893},
year = {2019},
url = {http://papers.nips.cc/paper/8912-likelihood-free-overcomplete-ica-and-applications-in-causal-discovery.html},
}
@article{DingLi18,
title = {Causal Inference: A Missing Data Perspective},
author = {Ding, Peng and Li, Fan},
journal = {Statistical Science},
volume = {33},
number = {2},
pages = {214--237},
year = {2018},
url = {https://doi.org/10.1214/18-STS645},
}
@article{EfronFeldman91,
title = {Compliance as an Explanatory Variable in Clinical Trials (with discussion)},
author = {B. Efron and D. Feldman},
journal = {Journal of the American Statistical Association},
volume = {86},
number = {413},
pages = {9--17},
year = {1991},
url = {http://www.jstor.org/stable/2289707},
}
@article{FrangakisRubin02,
title = {Principal Stratification in Causal Inference},
author = {Frangakis, Constantine E. and Rubin, Donald B.},
journal = {Biometrics},
volume = {58},
number = {1},
pages = {21--29},
url = {https://onlinelibrary.wiley.com/doi/abs/10.1111/j.0006-341X.2002.00021.x},
year = {2002},
}
@inproceedings{GargPLTCB19,
title = {Counterfactual Fairness in Text Classification through Robustness},
author = {Garg, Sahaj and Perot, Vincent and Limtiaco, Nicole and Taly, Ankur and Chi, Ed H. and Beutel, Alex},
booktitle = {Proceedings of the 2019 {AAAI/ACM} Conference on {AI}, Ethics, and Society},
pages = {219–-226},
year = {2019},
url = {https://doi.org/10.1145/3306618.3317950},
}
@article{GengLLM19,
title = {Evaluation of Causal Effects and Local Structure Learning of Causal Networks},
author = {Geng, Zhi and Liu, Yue and Liu, Chunchen and Miao, Wang},
journal = {Annual Review of Statistics and Its Application},
volume = {6},
number = {1},
pages = {103--124},
year = {2019},
url = {https://doi.org/10.1146/annurev-statistics-030718-105312},
keywords = {review},
}
@inproceedings{GentzelGJ19,
title = {The Case for Evaluating Causal Models Using Interventional Measures and Empirical Data},
author = {Gentzel, Amanda and Garant, Dan and Jensen, David},
booktitle = {Advances in Neural Information Processing Systems 32},
pages = {11722--11732},
year = {2019},
url = {http://papers.nips.cc/paper/9345-the-case-for-evaluating-causal-models-using-interventional-measures-and-empirical-data.html},
}
@inproceedings{GultchinKKS20,
title = {Differentiable Causal Backdoor Discovery},
author = {Gultchin, Limor and Kusner, Matt and Kanade, Varun and Silva, Ricardo},
booktitle = {Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics},
pages = {3970--3979},
year = {2020},
url = {http://proceedings.mlr.press/v108/gultchin20a.html},
}
@article{Heckman89,
title = {Causal Inference and Nonrandom Samples},
author = {James J. Heckman},
journal = {Journal of Educational Statistics},
volume = {14},
number = {2},
pages = {159--168},
year = {1989},
url = {https://doi.org/10.3102/10769986014002159},
}
@article{HeinzeDemlMM18,
title = {Causal Structure Learning},
author = {Heinze-Deml, Christina and Maathuis, Marloes H. and Meinshausen, Nicolai},
journal = {Annual Review of Statistics and Its Application},
volume = {5},
number = {1},
pages = {371--391},
year = {2018},
url = {https://doi.org/10.1146/annurev-statistics-031017-100630},
keywords = {review},
}
@article{Holland86,
title = {Statistics and Causal Inference (with discussion)},
author = {Paul W. Holland},
journal = {Journal of the American Statistical Association},
volume = {81},
number = {396},
pages = {945--960},
year = {1986},
url = {https://www.tandfonline.com/doi/abs/10.1080/01621459.1986.10478354},
}
@article{HuangValtorta08,
title = {On the completeness of an identifiability algorithm for semi-Markovian models},
author = {Yimin Huang and Marco Valtorta},
journal = {Annals of Mathematics and Artificial Intelligence},
volume = {54},
pages = {363-–408},
year = {2008},
url = {https://doi.org/10.1007/s10472-008-9101-x},
}
@inproceedings{JaberZB19,
title = {Identification of Conditional Causal Effects under Markov Equivalence},
author = {Jaber, Amin and Zhang, Jiji and Bareinboim, Elias},
booktitle = {Advances in Neural Information Processing Systems 32},
pages = {11516--11524},
year = {2019},
url = {http://papers.nips.cc/paper/9327-identification-of-conditional-causal-effects-under-markov-equivalence.html},
}
@inproceedings{Janzing19,
title = {Causal Regularization},
author = {Janzing, Dominik},
booktitle = {Advances in Neural Information Processing Systems 32},
pages = {12704--12714},
year = {2019},
url = {http://papers.nips.cc/paper/9432-causal-regularization.html},
}
@inproceedings{JanzingMB20,
title = {Feature relevance quantification in explainable AI: A causal problem},
author = {Janzing, Dominik and Minorics, Lenon and Bloebaum, Patrick},
booktitle = {Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics},
pages = {2907--2916},
year = {2020},
url = {http://proceedings.mlr.press/v108/janzing20a.html},
}
@inproceedings{JohanssonSS16,
title = {Learning Representations for Counterfactual Inference},
author = {Fredrik Johansson and Uri Shalit and David Sontag},
booktitle = {Proceedings of The 33rd International Conference on Machine Learning},
pages = {3020--3029},
year = {2016},
url = {http://proceedings.mlr.press/v48/johansson16.html},
}
@inproceedings{KallusMU18,
title = {Causal Inference with Noisy and Missing Covariates via Matrix Factorization},
author = {Kallus, Nathan and Mao, Xiaojie and Udell, Madeleine},
booktitle = {Advances in Neural Information Processing Systems 31},
pages = {6921--6932},
year = {2018},
url = {http://papers.nips.cc/paper/7924-causal-inference-with-noisy-and-missing-covariates-via-matrix-factorization.html},
}
@inproceedings{Kallus20,
title = {DeepMatch: Balancing Deep Covariate Representations for Causal Inference Using Adversarial Training},
author = {Nathan Kallus},
booktitle = {Proceedings of the 37th International Conference on Machine Learning},
year = {2020},
url = {https://icml.cc/virtual/2020/poster/6247},
}
@inproceedings{KarimiBBV20,
title = {Model-Agnostic Counterfactual Explanations for Consequential Decisions},
author = {Karimi, Amir-Hossein and Barthe, Gilles and Balle, Borja and Valera, Isabel},
booktitle = {Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics},
pages = {895--905},
year = {2020},
url = {http://proceedings.mlr.press/v108/karimi20a.html},
}
@inproceedings{KaushikHL20,
title = {Learning The Difference That Makes A Difference With Counterfactually-Augmented Data},
author = {Divyansh Kaushik and Eduard Hovy and Zachary Lipton},
booktitle = {International Conference on Learning Representation},
year = {2020},
url = {https://iclr.cc/virtual_2020/poster_Sklgs0NFvr.html},
}
@article{KennedyMDBS19,
title = {Handling Missing Data in Instrumental Variable Methods for Causal Inference},
author = {Kennedy, Edward H. and Mauro, Jacqueline A. and Daniels, Michael J. and Burns, Natalie and Small, Dylan S.},
journal = {Annual Review of Statistics and Its Application},
volume = {6},
number = {1},
pages = {125--148},
year = {2019},
url = {https://doi.org/10.1146/annurev-statistics-031017-100353},
keywords = {review},
}
@inproceedings{KilbertusRPHJS17,
title = {Avoiding Discrimination through Causal Reasoning},
author = {Kilbertus, Niki and Rojas Carulla, Mateo and Parascandolo, Giambattista and Hardt, Moritz and Janzing, Dominik and Sch\"{o}lkopf, Bernhard},
booktitle = {Advances in Neural Information Processing Systems 30},
pages = {656--666},
year = {2017},
url = {http://papers.nips.cc/paper/6668-avoiding-discrimination-through-causal-reasoning.html},
}
@inproceedings{KocaogluSDV18,
title = {Causal{GAN}: Learning Causal Implicit Generative Models with Adversarial Training},
author = {Murat Kocaoglu and Christopher Snyder and Alexandros G. Dimakis and Sriram Vishwanath},
booktitle = {International Conference on Learning Representations},
year = {2018},
url = {https://openreview.net/forum?id=BJE-4xW0W},
}
@inproceedings{KocaogluJSB19,
title = {Characterization and Learning of Causal Graphs with Latent Variables from Soft Interventions},
author = {Kocaoglu, Murat and Jaber, Amin and Shanmugam, Karthikeyan and Bareinboim, Elias},
booktitle = {Advances in Neural Information Processing Systems 32},
pages = {14369--14379},
year = {2019},
url = {http://papers.nips.cc/paper/9581-characterization-and-learning-of-causal-graphs-with-latent-variables-from-soft-interventions.html},
}
@article{KohlerKS19,
title = {Nonprobability Sampling and Causal Analysis},
author = {Kohler, Ulrich and Kreuter, Frauke and Stuart, Elizabeth A.},
journal = {Annual Review of Statistics and Its Application},
volume = {6},
number = {1},
pages = {149--172},
year = {2019},
url = {https://doi.org/10.1146/annurev-statistics-030718-104951},
keywords = {review},
}
@inproceedings{KuangSSWCDPR20,
title = {Ivy: Instrumental Variable Synthesis for Causal Inference},
author = {Kuang, Zhaobin and Sala, Frederic and Sohoni, Nimit and Wu, Sen and C{\'o}rdova-Palomera, Aldo and Dunnmon, Jared and Priest, James and Re, Christopher},
booktitle = {Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics},
pages = {398--410},
year = {2020},
url = {http://proceedings.mlr.press/v108/kuang20a.html},
}
@inproceedings{KusnerSSW16,
title = {Private Causal Inference},
author = {Matt J. Kusner and Yu Sun and Karthik Sridharan and Kilian Q. Weinberger},
booktitle = {Proceedings of the 19th International Conference on Artificial Intelligence and Statistics},
pages = {1308--1317},
year = {2016},
url = {http://proceedings.mlr.press/v51/kusner16.html},
}
@inproceedings{KusnerLRS17,
title = {Counterfactual Fairness},
author = {Kusner, Matt J and Loftus, Joshua and Russell, Chris and Silva, Ricardo},
booktitle = {Advances in Neural Information Processing Systems 30},
pages = {4066--4076},
year = {2017},
url = {http://papers.nips.cc/paper/6995-counterfactual-fairness.html},
}
@inproceedings{LiptonMC18,
title = {Does mitigating ML's impact disparity require treatment disparity?},
author = {Lipton, Zachary and McAuley, Julian and Chouldechova, Alexandra},
booktitle = {Advances in Neural Information Processing Systems 31},
pages = {8125--8135},
year = {2018},
url = {http://papers.nips.cc/paper/8035-does-mitigating-mls-impact-disparity-require-treatment-disparity.html},
}
@article{Little21,
title = {Missing Data Assumptions},
author = {Little, Roderick J.},
journal = {Annual Review of Statistics and Its Application},
volume = {8},
number = {1},
year = {2021},
url = {https://doi.org/10.1146/annurev-statistics-040720-031104},
keywords = {review},
}
@article{LittleRubin00,
title = {Causal Effects in Clinical and Epidemiological Studies Via Potential Outcomes: Concepts and Analytical Approaches},
author = {Little, Roderick J. and Rubin, Donald B.},
journal = {Annual Review of Public Health},
volume = {21},
number = {1},
pages = {121--145},
url = {https://doi.org/10.1146/annurev.publhealth.21.1.121},
year = {2000},
}
@inproceedings{MadrasCPZ19,
title = {Fairness through Causal Awareness: Learning Causal Latent-Variable Models for Biased Data},
author = {Madras, David and Creager, Elliot and Pitassi, Toniann and Zemel, Richard},
booktitle = {Proceedings of the Conference on Fairness, Accountability, and Transparency},
pages = {349–-358},
year = {2019},
url = {https://doi.org/10.1145/3287560.3287564},
}
@inproceedings{MillerMH20,
title = {Strategic Classification is Causal Modeling in Disguise},
author = {John Miller and Smitha Milli and Moritz Hardt},
booktitle = {Proceedings of the 37th International Conference on Machine Learning},
year = {2020},
url = {https://icml.cc/virtual/2020/poster/6779},
}
@inproceedings{MitrovicST18,
title = {Causal Inference via Kernel Deviance Measures},
author = {Mitrovic, Jovana and Sejdinovic, Dino and Teh, Yee Whye},
booktitle = {Advances in Neural Information Processing Systems 31},
pages = {6986--6994},
year = {2018},
url = {http://papers.nips.cc/paper/7930-causal-inference-via-kernel-deviance-measures.html},
}
@inproceedings{NessPV19,
title = {Integrating Markov processes with structural causal modeling enables counterfactual inference in complex systems},
author = {Ness, Robert and Paneri, Kaushal and Vitek, Olga},
booktitle = {Advances in Neural Information Processing Systems 32},
pages = {14234--14244},
year = {2019},
url = {http://papers.nips.cc/paper/9569-integrating-markov-processes-with-structural-causal-modeling-enables-counterfactual-inference-in-complex-systems.html},
}
@article{NeymanDS90,
title = {On the Application of Probability Theory to Agricultural Experiments. Essay on Principles. Section 9.},
author = {Jerzy Splawa-Neyman and D. M. Dabrowska and T. P. Speed},
journal = {Statistical Science},
number = {4},
pages = {465--472},
volume = {5},
year = {1990},
url = {http://www.jstor.org/stable/2245382},
}
@inproceedings{ParascandoloKRCS18,
title = {Learning Independent Causal Mechanisms},
author = {Parascandolo, Giambattista and Kilbertus, Niki and Rojas-Carulla, Mateo and Sch{\"o}lkopf, Bernhard},
booktitle = {Proceedings of the 35th International Conference on Machine Learning},
pages = {4036--4044},
year = {2018},
url = {http://proceedings.mlr.press/v80/parascandolo18a.html},
}
@article{Pearl95,
title = {Causal diagrams for empirical research},
author = {Pearl, Judea},
journal = {Biometrika},
volume = {82},
number = {4},
pages = {669--688},
year = {1995},
url = {https://doi.org/10.1093/biomet/82.4.669},
}
@inproceedings{PearlRobins95,
title = {Probabilistic evaluation of sequential plans from causal models with hidden variables},
author = {Pearl, Judea and James Robins},
booktitle = {Proceedings of the Eleventh conference on Uncertainty in artificial intelligence},
pages = {444–-453},
year = {1995},
url = {https://dl.acm.org/doi/10.5555/2074158.2074209},
}
@inproceedings{Pearl01,
title = {Direct and Indirect Effects},
author = {Pearl, Judea},
booktitle = {Proceedings of the Seventeenth Conference on Uncertainty in Artificial Intelligence},
pages = {411--420},
year = {2001},
url = {https://dl.acm.org/doi/10.5555/2074022.2074073},
}
@article{Pearl09,
title = {Causal inference in statistics: An overview},
author = {Pearl, Judea},
journal = {Statistics Surveys},
volume = {3},
pages = {96--146},
year = {2009},
url = {https://doi.org/10.1214/09-SS057},
keywords = {review},
}
@inproceedings{Pearl12,
title = {The Do-Calculus Revisited},
author = {Pearl, Judea},
booktitle = {Proceedings of the Twenty-Eighth Conference on Uncertainty in Artificial Intelligence},
pages = {3--11},
year = {2012},
url = {https://arxiv.org/abs/1210.4852},
}
@article{Pearl19,
title = {The Seven Tools of Causal Inference, with Reflections on Machine Learning},
author = {Pearl, Judea},
journal = {Communications of {ACM}},
volume = {62},
number = {3},
pages = {54-–60},
year = {2019},
url = {https://doi.org/10.1145/3241036},
keywords = {review},
}
@inproceedings{PeysakhovichKL19,
title = {Robust Multi-agent Counterfactual Prediction},
author = {Peysakhovich, Alexander and Kroer, Christian and Lerer, Adam},
booktitle = {Advances in Neural Information Processing Systems 32},
pages = {3083--3093},
year = {2019},
url = {http://papers.nips.cc/paper/8572-robust-multi-agent-counterfactual-prediction.html},
}
@article{PrattSchlaifer88,
title = {On the interpretation and observation of laws},
author = {John W. Pratt and Robert Schlaifer},
journal = {Journal of Econometrics},
volume = {39},
number = {1},
pages = {23--52},
year = {1988},
url = {https://doi.org/10.1016/0304-4076(88)90039-5},
}
@article{RaudenbushSchwartz20,
title = {Randomized Experiments in Education, with Implications for Multilevel Causal Inference},
author = {Raudenbush, Stephen W. and Schwartz, Daniel},
journal = {Annual Review of Statistics and Its Application},
volume = {7},
number = {1},
pages = {177--208},
year = {2020},
url = {https://doi.org/10.1146/annurev-statistics-031219-041205},
keywords = {review},
}
@article{Rosenbaum20,
title = {Modern Algorithms for Matching in Observational Studies},
author = {Rosenbaum, Paul R.},
journal = {Annual Review of Statistics and Its Application},
volume = {7},
number = {1},
pages = {143--176},
year = {2020},
url = {https://doi.org/10.1146/annurev-statistics-031219-041058},
keywords = {review},
}
@article{RosenbaumRubin83,
title = {The central role of the propensity score in observational studies for causal effects},
author = {Rosenbaum, Paul R. and Rubin, Donald B.},
journal = {Biometrika},
volume = {70},
pages = {41--55},
year = {1983},
url = {https://doi.org/10.1093/biomet/70.1.41},
}
@article{RosenbaumRubin84,
title = {Reducing Bias in Observational Studies Using Subclassification on the Propensity Score},
author = {Paul R. Rosenbaum and Donald B. Rubin},
journal = {Journal of the American Statistical Association},
volume = {79},
number = {387},
pages = {516--524},
year = {1984},
url = {https://amstat.tandfonline.com/doi/abs/10.1080/01621459.1984.10478078},
}
@article{RosenbaumRubin85,
title = {Constructing a Control Group Using Multivariate Matched Sampling Methods That Incorporate the Propensity Score},
author = {Paul R. Rosenbaum and Donald B. Rubin},
journal = {The American Statistician},
volume = {39},
number = {1},
pages = {33--38},
year = {1985},
url = {https://amstat.tandfonline.com/doi/abs/10.1080/00031305.1985.10479383},
}
@article{Rubin72,
title = {Estimating causal effects of treatments in randomized and nonrandomized studies},
author = {Rubin, Donald},
journal = {Journal of Educational Psychology},
volume = {66},
number = {5},
pages = {688--701},
url = {https://doi.org/10.1037/h0037350},
year = {1972},
}
@article{Rubin78,
title = {Bayesian Inference for Causal Effects: The Role of Randomization},
author = {Donald B. Rubin},
journal = {The Annals of Statistics},
volume = {6},
number = {1},
pages = {34--58},
year = {1978},
url = {http://www.jstor.org/stable/2958688},
}
@article{Rubin96,
title = {Multiple Imputation after 18+ Years},
author = {Donald B. Rubin},
journal = {Journal of the American Statistical Association},
volume = {91},
number = {434},
pages = {473--489},
year = {1996},
url = {https://www.tandfonline.com/doi/abs/10.1080/01621459.1996.10476908},
}
@article{Rubin04a,
title = {Teaching Statistical Inference for Causal Effects in Experiments and Observational Studies},
author = {Donald B. Rubin},
journal = {Journal of Educational and Behavioral Statistics},
volume = {29},
number = {3},
pages = {343--367},
year = {2004},
url = {http://www.jstor.org/stable/3701358},
}
@article{Rubin04b,
title = {Direct and Indirect Causal Effects via Potential Outcomes (with discussion)},
author = {Rubin, Donald B.},
journal = {Scandinavian Journal of Statistics},
volume = {31},
number = {2},
pages = {161--170},
url = {https://onlinelibrary.wiley.com/doi/abs/10.1111/j.1467-9469.2004.02-123.x},
year = {2004},
}
@article{Rubin05,
title = {Causal Inference Using Potential Outcomes},
author = {Donald B Rubin},
journal = {Journal of the American Statistical Association},
volume = {100},
number = {469},
pages = {322--331},
year = {2005},
url = {https://doi.org/10.1198/016214504000001880},
}
@article{Rubin06,
title = {Causal Inference Through Potential Outcomes and Principal Stratification: Application to Studies with ``Censoring'' Due to Death},
author = {Rubin, Donald B.},
journal = {Statistical Science},
volume = {21},
number = {3},
pages = {299--309},
year = {2006},
url = {https://doi.org/10.1214/088342306000000114},
}
@article{RubinWaterman06,
title = {Estimating the Causal Effects of Marketing Interventions Using Propensity Score Methodology},
author = {Rubin, Donald B. and Waterman, Richard P.},
journal = {Statistical Science},
volume = {21},
number = {2},
pages = {206--222},
year = {2006},
url = {https://doi.org/10.1214/088342306000000259},
}
@inproceedings{RussellKLS17,
title = {When Worlds Collide: Integrating Different Counterfactual Assumptions in Fairness},
author = {Russell, Chris and Kusner, Matt J and Loftus, Joshua and Silva, Ricardo},
booktitle = {Advances in Neural Information Processing Systems 30},
pages = {6414--6423},
year = {2017},
url = {http://papers.nips.cc/paper/7220-when-worlds-collide-integrating-different-counterfactual-assumptions-in-fairness.html},
}
@article{SanderPR99,
title = {Causal diagrams for epidemiologic research},
author = {Greenland Sander and Pearl, Judea and Robins, James M.},
journal = {Epidemiology},
volume = {10},
number = {1},
pages = {37--48},
year = {1999},
url = {https://journals.lww.com/epidem/Abstract/1999/01000/Causal_Diagrams_for_Epidemiologic_Research.8.aspx},
}
@article {ScholkopfHWFJSP16,
title = {Modeling confounding by half-sibling regression},
author = {Sch{\"o}lkopf, Bernhard and Hogg, David W. and Wang, Dun and Foreman-Mackey, Daniel and Janzing, Dominik and Simon-Gabriel, Carl-Johann and Peters, Jonas},
journal = {Proceedings of the National Academy of Sciences},
volume = {113},
number = {27},
pages = {7391--7398},
year = {2016},
url = {https://www.pnas.org/content/113/27/7391},
}
@inproceedings{SchwabKarlen19,
title = {CXPlain: Causal Explanations for Model Interpretation under Uncertainty},
author = {Schwab, Patrick and Karlen, Walter},
booktitle = {Advances in Neural Information Processing Systems 32},
pages = {10220--10230},
year = {2019},
url = {http://papers.nips.cc/paper/9211-cxplain-causal-explanations-for-model-interpretation-under-uncertainty.html},
}
@inproceedings{ShermanShpitser18,
title = {Identification and Estimation of Causal Effects from Dependent Data},
author = {Sherman, Eli and Shpitser, Ilya},
booktitle = {Advances in Neural Information Processing Systems 31},
pages = {9424--9435},
year = {2018},
url = {http://papers.nips.cc/paper/8153-identification-and-estimation-of-causal-effects-from-dependent-data.html},
}
@inproceedings{ShpitserPearl06a,
title = {Identification of conditional interventional distributions},
author = {Ilya Shpitser and Judea Pearl},
booktitle = {Proceedings of the Twenty-Second Conference on Uncertainty in Artificial Intelligence},
pages = {437–-444},
year = {2006},
url = {https://arxiv.org/abs/1206.6876},
}
@inproceedings{ShpitserPearl06b,
title = {Identification of joint interventional distributions in recursive semi-Markovian causal models},
author = {Ilya Shpitser and Judea Pearl},
booktitle = {Proceedings of the 21st national conference on Artificial intelligence},
pages = {1219-–1226},
year = {2006},
url = {https://www.aaai.org/Papers/AAAI/2006/AAAI06-191.pdf},
}
@inproceedings{ShpitserPearl07,
title = {What counterfactuals can be tested},
author = {Ilya Shpitser and Judea Pearl},
booktitle = {Proceedings of the Twenty-Third Conference on Uncertainty in Artificial Intelligence},
pages = {352–-359},
year = {2007},
url = {https://arxiv.org/abs/1206.5294},
}
@inproceedings{SnowsillFDBC11,
title = {Refining Causality: Who Copied from Whom?},
author = {Snowsill, Tristan Mark and Fyson, Nick and De Bie, Tijl and Cristianini, Nello},
booktitle = {Proceedings of the 17th {ACM SIGKDD} International Conference on Knowledge Discovery and Data Mining},
pages = {466–-474},
year = {2011},
url = {https://doi.org/10.1145/2020408.2020483},
}
@article{Sobel96,
title = {An Introduction to Causal Inference},
author = {Michael E. Sobel},
journal = {Sociological Methods \& Research},
volume = {24},
number = {3},
pages = {353--379},
year = {1996},
url = {https://doi.org/10.1177/0049124196024003004},
}
@inproceedings{SuterMSB19,
title = {Robustly Disentangled Causal Mechanisms: Validating Deep Representations for Interventional Robustness},
author = {Suter, Raphael and Miladinovic, Djordje and Sch{\"o}lkopf, Bernhard and Bauer, Stefan},
booktitle = {Proceedings of the 36th International Conference on Machine Learning},
pages = {6056--6065},
year = {2019},
url = {http://proceedings.mlr.press/v97/suter19a.html},
}
@inproceedings{TagasovskaCDV20,
title = {Distinguishing Cause from Effect Using Quantiles: Bivariate Quantile Causal Discovery},
author = {Natasa Tagasovska and Val{\'e}rie Chavez-Demoulin and Thibault Vatter},
booktitle = {Proceedings of the 37th International Conference on Machine Learning},
year = {2020},
url = {https://icml.cc/virtual/2020/poster/6405},
}
@inproceedings{TeshimaSS20,
title = {Few-shot Domain Adaptation by Causal Mechanism Transfer},
author = {Takeshi Teshima and Issei Sato and Masashi Sugiyama},
booktitle = {Proceedings of the 37th International Conference on Machine Learning},
year = {2020},
url = {https://icml.cc/virtual/2020/poster/5935},
}
@inproceedings{TianPearl02a,
title = {A general identification condition for causal effects},
author = {Jin Tian and Judea Pearl},
booktitle = {Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence}
pages = {567-–573},
year = {2002},
url = {https://dl.acm.org/doi/10.5555/777092.777180},
}
@inproceedings{TianPearl02b,
title = {On the testable implications of causal models with hidden variables},
author = {Jin Tian and Judea Pearl},
booktitle = {Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence}
pages = {519–-527},
year = {2002},
url = {https://dl.acm.org/doi/10.5555/2073876.2073938},
}
@inproceedings{TikkaHK19,
title = {Identifying Causal Effects via Context-specific Independence Relations},
author = {Tikka, Santtu and Hyttinen, Antti and Karvanen, Juha},
booktitle = {Advances in Neural Information Processing Systems 32},
pages = {2804--2814},
year = {2019},
url = {http://papers.nips.cc/paper/8547-identifying-causal-effects-via-context-specific-independence-relations.html},
}
@inproceedings{TopleSN20,
title = {Alleviating Privacy Attacks via Causal Learning},
author = {Shruti Tople and Amit Sharma and Aditya Nori},
booktitle = {Proceedings of the 37th International Conference on Machine Learning},
year = {2020},
url = {https://icml.cc/virtual/2020/poster/6346},
}
@article {Varian16,
title = {Causal inference in economics and marketing},
author = {Varian, Hal R.},
journal = {Proceedings of the National Academy of Sciences},
volume = {113},
number = {27},
pages = {7310--7315},
year = {2016},
url = {https://www.pnas.org/content/113/27/7310},
}
@article{WangBlei19,
title = {The Blessings of Multiple Causes},
author = {Yixin Wang and David M. Blei},
journal = {Journal of the American Statistical Association},
volume = {114},
number = {528},
pages = {1574--1596},
year = {2019},
url = {https://doi.org/10.1080/01621459.2019.1686987},
}
@inproceedings{WangSBU18,
title = {Direct Estimation of Differences in Causal Graphs},
author = {Wang, Yuhao and Squires, Chandler and Belyaeva, Anastasiya and Uhler, Caroline},
booktitle = {Advances in Neural Information Processing Systems 31},
pages = {3770--3781},
year = {2018},
url = {http://papers.nips.cc/paper/7634-direct-estimation-of-differences-in-causal-graphs.html},
}
@inproceedings{WangUC19,
title = {Repairing without Retraining: Avoiding Disparate Impact with Counterfactual Distributions},
author = {Wang, Hao and Ustun, Berk and Calmon, Flavio},
booktitle = {Proceedings of the 36th International Conference on Machine Learning},
pages = {6618--6627},
year = {2019},
url = {http://proceedings.mlr.press/v97/wang19l.html},
}
@article{Ware89,
title = {Investigating Therapies of Potentially Great Benefit: {ECMO} (with discussion)},
author = {James H. Ware},
journal = {Statistical Science},
volume = {4},
number = {4},
pages = {298--306},
year = {1989},
url = {http://www.jstor.org/stable/2245829},
}
@article{WinshipMorgan99,
title = {The estimation of causal effects from observational data},
author = {Winship, Christopher and Morgan, Stephen L.},
journal = {Annual Review of Sociology},
volume = {25},
number = {1},
pages = {659--706},
year = {1999},
url = {https://doi.org/10.1146/annurev.soc.25.1.659},
}
@inproceedings{WuZWT19,
title = {PC-Fairness: A Unified Framework for Measuring Causality-based Fairness},
author = {Wu, Yongkai and Zhang, Lu and Wu, Xintao and Tong, Hanghang},
booktitle = {Advances in Neural Information Processing Systems 32},
pages = {3404--3414},
year = {2019},
url = {http://papers.nips.cc/paper/8601-pc-fairness-a-unified-framework-for-measuring-causality-based-fairness.html},
}
@inproceedings{WuFukumizu20,
title = {Causal Mosaic: Cause-Effect Inference via Nonlinear ICA and Ensemble Method},
author = {Wu, Pengzhou and Fukumizu, Kenji},
booktitle = {Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics},
pages = {1157--1167},
year = {2020},
url = {http://proceedings.mlr.press/v108/wu20b.html},
}
@inproceedings{ZhangBareinboim18,
title = {Equality of Opportunity in Classification: A Causal Approach},
author = {Zhang, Junzhe and Bareinboim, Elias},
booktitle = {Advances in Neural Information Processing Systems 31},
pages = {3671--3681},
year = {2018},
url = {http://papers.nips.cc/paper/7625-equality-of-opportunity-in-classification-a-causal-approach.html},
}
@inproceedings{ZhengDARX20,
title = {Learning Sparse Nonparametric DAGs},
author = {Zheng, Xun and Dan, Chen and Aragam, Bryon and Ravikumar, Pradeep and Xing, Eric},
pages = {3414--3425},
booktitle = {Proceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics},
year = {2020},
url = {http://proceedings.mlr.press/v108/zheng20a.html},
}
@inproceedings{ZhengARX18,
title = {DAGs with NO TEARS: Continuous Optimization for Structure Learning},
author = {Zheng, Xun and Aragam, Bryon and Ravikumar, Pradeep K and Xing, Eric P},
booktitle = {Advances in Neural Information Processing Systems 31},
pages = {9472--9483},
year = {2018},
url = {http://papers.nips.cc/paper/8157-dags-with-no-tears-continuous-optimization-for-structure-learning.html},
}