-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathreadme.html
699 lines (608 loc) · 45.5 KB
/
readme.html
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
<!DOCTYPE HTML PUBLIC "-//W3C//DTD HTML 3.2 Final//EN">
<html>
<head>
<title>Index of /teaching/CausalInf2021</title>
</head>
<body>
<h1>Index of /teaching/CausalInf2021</h1>
<pre><img src="/icons/blank.gif" alt="Icon "> <a href="?C=N;O=D">Name</a> <a href="?C=M;O=A">Last modified</a> <a href="?C=S;O=A">Size</a> <a href="?C=D;O=A">Description</a><hr><img src="/icons/back.gif" alt="[PARENTDIR]"> <a href="/teaching/">Parent Directory</a> -
<img src="/icons/layout.gif" alt="[ ]"> <a href="10%20-%20Causal%20Discovery%20from%20Observational%20Data.pdf">10 - Causal Discovery from Observational Data.pdf</a> 2021-02-02 12:36 3.5M
<img src="/icons/layout.gif" alt="[ ]"> <a href="11%20-%20Causal%20Discovery%20from%20Interventions.pdf">11 - Causal Discovery from Interventions.pdf</a> 2021-02-02 12:36 2.9M
<img src="/icons/layout.gif" alt="[ ]"> <a href="12%20-%20Transfer%20Learning%20and%20Transportability.pdf">12 - Transfer Learning and Transportability.pdf</a> 2021-02-02 12:36 3.2M
<img src="/icons/layout.gif" alt="[ ]"> <a href="14%20-%20Counterfactuals%20and%20Mediation.pdf">14 - Counterfactuals and Mediation.pdf</a> 2021-02-02 12:36 3.1M
<img src="/icons/layout.gif" alt="[ ]"> <a href="2%20-%20Potential%20Outcomes.pdf">2 - Potential Outcomes.pdf</a> 2021-02-02 12:36 5.9M
<img src="/icons/layout.gif" alt="[ ]"> <a href="3%20-%20The%20Flow%20of%20Association%20and%20Causation%20in%20Graphs.pdf">3 - The Flow of Association and Causation in Graphs.pdf</a> 2021-02-02 12:36 3.8M
<img src="/icons/layout.gif" alt="[ ]"> <a href="4%20-%20Causal%20Models.pdf">4 - Causal Models.pdf</a> 2021-02-02 12:36 5.8M
<img src="/icons/layout.gif" alt="[ ]"> <a href="5%20-%20Identification.pdf">5 - Identification.pdf</a> 2021-02-02 12:36 4.3M
<img src="/icons/layout.gif" alt="[ ]"> <a href="6%20-%20Estimation.pdf">6 - Estimation.pdf</a> 2021-02-02 12:36 4.0M
<img src="/icons/layout.gif" alt="[ ]"> <a href="7%20-%20Unobserved%20Confounding.pdf">7 - Unobserved Confounding.pdf</a> 2021-02-02 12:36 7.5M
<img src="/icons/layout.gif" alt="[ ]"> <a href="8%20-%20Instrumental%20Variables.pdf">8 - Instrumental Variables.pdf</a> 2021-02-02 12:36 3.4M
<img src="/icons/layout.gif" alt="[ ]"> <a href="9%20-%20Difference-in-Differences.pdf">9 - Difference-in-Differences.pdf</a> 2021-02-02 12:36 3.1M
<img src="/icons/unknown.gif" alt="[ ]"> <a href="CausalCourse%20Syllabus.docx">CausalCourse Syllabus.docx</a> 2021-01-08 14:51 8.4K
<img src="/icons/unknown.gif" alt="[ ]"> <a href="CausalityLecture-30nov2020.pptx">CausalityLecture-30nov2020.pptx</a> 2021-01-22 14:26 36M
<img src="/icons/layout.gif" alt="[ ]"> <a href="Introduction_to_Causal_Inference-Dec17_2020-Neal.pdf">Introduction_to_Causal_Inference-Dec17_2020-Neal.pdf</a> 2021-01-22 14:16 1.0M
<img src="/icons/text.gif" alt="[TXT]"> <a href="presentations.jl.html">presentations.jl.html</a> 2021-02-02 13:04 19K
<img src="/icons/layout.gif" alt="[ ]"> <a href="probability_cheatsheet.pdf">probability_cheatsheet.pdf</a> 2021-01-26 12:44 789K
<hr></pre>
<!DOCTYPE html>
<html lang="en">
<!-- Beautiful Jekyll | MIT license | Copyright Dean Attali 2016 -->
<head>
<meta charset="utf-8" />
<meta http-equiv="X-UA-Compatible" content="IE=edge">
<meta name="viewport" content="width=device-width, initial-scale=1.0, maximum-scale=1.0, viewport-fit=cover">
<title>Introduction to Causal Inference</title>
<meta name="author" content="Brady Neal" />
<meta name="description"
content="Introduction to Causal Inference. A free online course on causal inference from a machine learning perspective.">
<link rel="alternate" type="application/rss+xml"
title="Brady Neal - Causality Blog - Brady Neal's personal website and blog about causal inference and machine learning."
href="https://www.bradyneal.com/feed.xml" />
<link rel="stylesheet" href="//maxcdn.bootstrapcdn.com/font-awesome/4.6.0/css/font-awesome.min.css" />
<style>
table {
border-collapse: collapse;
}
</style>
<!--
<link rel="stylesheet" href="/css/bootstrap.min.css" />
<link rel="stylesheet" href="/css/bootstrap-social.css" />
<link rel="stylesheet" href="/css/main.css" /> -->
<link rel="stylesheet" href="//fonts.googleapis.com/css?family=Lora:400,700,400italic,700italic" />
<link rel="stylesheet"
href="//fonts.googleapis.com/css?family=Open+Sans:300italic,400italic,600italic,700italic,800italic,400,300,600,700,800" />
<!-- Facebook OpenGraph tags -->
<meta property="og:title" content="Introduction to Causal Inference" />
<meta property="og:description"
content="Introduction to Causal Inference. A free online course on causal inference from a machine learning perspective.">
<meta property="og:type" content="website" />
<meta property="og:url" content="https://www.bradyneal.com/causal-inference-course" />
<link rel="canonical" href="https://www.bradyneal.com/causal-inference-course" />
<meta property="og:image" content="https://www.bradyneal.com/img/favicon1250.png" />
<meta property="og:image:width" content="1200" />
<meta property="og:image:height" content="630" />
<!-- Twitter summary cards -->
<meta name="twitter:card" content="summary" />
<meta name="twitter:site" content="@" />
<meta name="twitter:creator" content="@" />
<meta name="twitter:title" content="Introduction to Causal Inference" />
<meta name="twitter:description"
content="Introduction to Causal Inference. A free online course on causal inference from a machine learning perspective.">
<meta name="twitter:image" content="https://www.bradyneal.com/img/favicon1250.png" />
<meta name="twitter:card" content="summary_large_image">
<!-- Citation tags -->
<!-- KaTeX (The fastest math typesetting library for the web) -->
<link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/[email protected]/dist/katex.min.css"
integrity="sha384-zB1R0rpPzHqg7Kpt0Aljp8JPLqbXI3bhnPWROx27a9N0Ll6ZP/+DiW/UqRcLbRjq" crossorigin="anonymous">
<!-- Commented below KaTeX js files that are only necessary for client-side in-browser rendering -->
<!-- <script defer src="https://cdn.jsdelivr.net/npm/[email protected]/dist/katex.min.js" integrity="sha384-y23I5Q6l+B6vatafAwxRu/0oK/79VlbSz7Q9aiSZUvyWYIYsd+qj+o24G5ZU2zJz" crossorigin="anonymous"></script>
<script defer src="https://cdn.jsdelivr.net/npm/[email protected]/dist/contrib/auto-render.min.js" integrity="sha384-kWPLUVMOks5AQFrykwIup5lo0m3iMkkHrD0uJ4H5cjeGihAutqP0yW0J6dpFiVkI" crossorigin="anonymous" onload="renderMathInElement(document.body);"></script> -->
</head>
<link rel="shortcut icon" href="/favicon.ico" type="image/x-icon" />
<link rel="apple-touch-icon" href="/apple-touch-icon.png" />
<link rel="apple-touch-icon" sizes="57x57" href="/apple-touch-icon-57x57.png" />
<link rel="apple-touch-icon" sizes="72x72" href="/apple-touch-icon-72x72.png" />
<link rel="apple-touch-icon" sizes="76x76" href="/apple-touch-icon-76x76.png" />
<link rel="apple-touch-icon" sizes="114x114" href="/apple-touch-icon-114x114.png" />
<link rel="apple-touch-icon" sizes="120x120" href="/apple-touch-icon-120x120.png" />
<link rel="apple-touch-icon" sizes="144x144" href="/apple-touch-icon-144x144.png" />
<link rel="apple-touch-icon" sizes="152x152" href="/apple-touch-icon-152x152.png" />
<link rel="apple-touch-icon" sizes="180x180" href="/apple-touch-icon-180x180.png" />
<body data-spy="scroll" data-target=".toc-nav" data-offset="150">
<!--
<nav class="navbar navbar-default navbar-fixed-top navbar-custom">
<div class="container-fluid">
<div class="navbar-header">
<button type="button" class="navbar-toggle" data-toggle="collapse" data-target="#main-navbar">
<span class="sr-only">Toggle navigation</span>
<span class="icon-bar"></span>
<span class="icon-bar"></span>
<span class="icon-bar"></span>
</button>
<a class="navbar-brand" href="https://www.bradyneal.com/">Brady Neal</a>
Change the website title in the upper-left corner to link to the About Me page, rather than the index/blog page
<a class="navbar-brand" href="https://www.bradyneal.com/aboutme">Brady Neal</a></div>
<div class="collapse navbar-collapse" id="main-navbar">
<ul class="nav navbar-nav navbar-right">
<li><a href="/causal-inference-course">Course</a></li>
<li><a href="/">Blog</a></li>
<li><a href="/aboutme">About Me</a></li>
<li><a href="https://scholar.google.ca/citations?user=cEpJgXYAAAAJ">Papers</a></li></ul>
</div> -->
</div>
</nav>
<!-- TODO this file has become a mess, refactor it -->
<header class="header-section ">
<div class="intro-header no-img">
<div class="container">
<div class="row">
<div class="col-lg-8 col-lg-offset-2 col-md-10 col-md-offset-1">
<div class="page-heading">
<h1 style="font-size:40px">Introduction to Causal Inference</h1>
<hr class="small">
<span class="page-subheading" style="font-size:20px">Spring 2021</span>
</div>
</div>
</div>
</div>
</div>
</header>
<div class="container" role="main">
<div class="row">
<div class="col-lg-12 col-md-12">
<!-- <div class="col-lg-10 col-lg-offset-1 col-md-10 col-md-offset-1"> -->
<!-- <div class="col-lg-10 col-lg-offset-1 col-md-10 col-md-offset-1"> -->
<p>This is the NYUSH causal inference course page. This webpage was more-or-less copied from <a
href="https://bradyneal.com">Brady Neal's Site</a>.
Although, the course text is written from a machine learning perspective, this course is meant to be
for anyone with the necessary <a href="#prerequisites">prerequisites</a> who is interested in
learning the basics of causality.
The class attempts to integrate insights from <a
href="https://www.bradyneal.com/which-causal-inference-book">many different fields</a> that
utilize causal inference such as epidemiology, economics, political science, machine learning, etc.
You can see the <a href="#course-schedule-tentative">tentative course schedule</a> below.</p>
<p>You can join the <a
href="https://chat.erlichlab.org/signup_user_complete/?id=d8phzf5nfib99j7f7ahti656gh">course
mattermost</a> where you can easily start discussions with other people who are interested in
causal inference. Email Profs. Erlich or Weslake to meet outside of class hours.
The main <a href="#course-textbook">textbook</a> we'll use for this course is <em>Introduction to
Causal Inference</em> (ICI), which is a book draft that I'll continually update throughout this
course.</p>
<h2 id="course-schedule-tentative">Course Schedule</h2>
<p><strong>Note about slides:</strong> they currently don't work well with Adobe Acrobat, though they
seem to work with other PDF viewers.</p>
<p><strong>Note about videos:</strong> The <a
html="https://www.youtube.com/playlist?list=PLoazKTcS0Rzb6bb9L508cyJ1z-U9iWkA0">videos for each
week</a> are broken up into several short videos. You must watch ALL of the videos for each week
as part of class preparation. </p>
<style>
th,
td {
max-width: 350px;
}
</style>
<table border>
<thead>
<tr>
<th>Week</th>
<th>Date</th>
<th>Topics</th>
<th>Lecture</th>
<th>Readings</th>
<th>Reading Group Paper</th>
</tr>
</thead>
<tbody>
<tr>
<td>1</td>
<td>Jan 26</td>
<td>Motivation<br />Course Preview<br />Course Information</td>
<td><a href="https://www.youtube.com/watch?v=CfzO4IEMVUk&list=PLoazKTcS0Rzb6bb9L508cyJ1z-U9iWkA0&index=1"
target="_blank" rel="noopener noreferrer">Video</a><br />
<a href="/slides/1%20-%20A%20Brief%20Introduction%20to%20Causal%20Inference.pdf"
download="">Slides</a><br />
<a href="https://www.youtube.com/watch?v=xj-tzrm5Src&list=PLoazKTcS0Rzb6bb9L508cyJ1z-U9iWkA0&index=6"
target="_blank" rel="noopener noreferrer">Info</a>
</td>
<td>Chapter 1 of ICI</td>
<td>None</td>
</tr>
<tr>
<td>2</td>
<td>Feb 2</td>
<td>Potential Outcomes<br />A Complete Example with Estimation</td>
<td><a href="https://www.youtube.com/watch?v=q8x9aetyok0&list=PLoazKTcS0Rzb6bb9L508cyJ1z-U9iWkA0&index=8"
target="_blank" rel="noopener noreferrer">Video</a><br />
<a href="/slides/2%20-%20Potential%20Outcomes.pdf" download="">Slides</a>
</td>
<td>Chapter 2 of ICI</td>
<td><a href="https://www.nature.com/articles/ijo200882" target="_blank"
rel="noopener noreferrer">Does obesity shorten life? The importance of well-defined
interventions to answer causal questions (Hernán & Taubman, 2008)</a></td>
</tr>
<tr>
<td>3</td>
<td>Feb 9</td>
<td>Graphical Models<br /></td>
<td><a href="https://www.youtube.com/watch?v=Go4EkHN_PcA&list=PLoazKTcS0Rzb6bb9L508cyJ1z-U9iWkA0&index=19"
target="_blank" rel="noopener noreferrer">Video</a><br />
<a href="/slides/3%20-%20The%20Flow%20of%20Association%20and%20Causation%20in%20Graphs.pdf"
download="">Slides</a>
</td>
<td>Chapter 3 of ICI</td>
<td><a href="https://ftp.cs.ucla.edu/pub/stat_ser/r483-reprint.pdf" target="_blank"
rel="noopener noreferrer">Does Obesity Shorten Life? Or is it the Soda? On
Non-manipulable Causes (Pearl, 2018)</a></td>
</tr>
<tr>
<td>4</td> <td> Feb 23</td>
<td>Backdoor Adjustment<br />Structural Causal Models</td>
<td><a href="https://www.youtube.com/watch?v=dB8r4Afmobo&list=PLoazKTcS0Rzb6bb9L508cyJ1z-U9iWkA0&index=28"
target="_blank" rel="noopener noreferrer">Video</a><br />
<a href="/slides/4%20-%20Causal%20Models.pdf" download="">Slides</a>
</td>
<td>Chapter 4 of ICI</td>
<td><a href="http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.644.1881&rep=rep1&type=pdf"
target="_blank" rel="noopener noreferrer">Single World Intervention Graphs: A Primer
(Richardson & Robins, 2013)</a></td>
</tr>
<tr>
<td>5</td><td> Mar 2</td>
<td>Randomized Experiments<br />Frontdoor
Adjustment<br /><i>do</i>-calculus<br />Graph-Based Identification</td>
<td><a href="https://www.youtube.com/watch?v=z91LnTDyhtI&list=PLoazKTcS0Rzb6bb9L508cyJ1z-U9iWkA0&index=37"
target="_blank" rel="noopener noreferrer">Video</a><br />
<a href="/slides/5%20-%20Identification.pdf" download="">Slides</a>
</td>
<td>Chapters 5-6 of ICI</td>
<td><a href="https://causalai.net/r60.pdf" target="_blank" rel="noopener noreferrer">On
Pearl's Hierarchy and the Foundations of Causal Inference (Bareinboim et al.,
2020)</a></td>
</tr>
<tr><td>6</td>
<td>Mar 9</td>
<td>Estimation<br /><a href="https://athey.people.stanford.edu" target="_blank"
rel="noopener noreferrer">Susan Athey</a><br />Estimating Heterogeneous
Treatment Effects<br /></td>
<td><a href="https://www.youtube.com/watch?v=YzcOYU-s2t4&list=PLoazKTcS0Rzb6bb9L508cyJ1z-U9iWkA0&index=42"
target="_blank" rel="noopener noreferrer">Video</a><br />
<a href="/slides/6%20-%20Estimation.pdf" download="">Slides</a><br />
<a href="https://www.youtube.com/watch?v=oZoizsX3bts&list=PLoazKTcS0RzZ1SUgeOgc6SWt51gfT80N0&index=7"
target="_blank" rel="noopener noreferrer">Guest Talk</a>
</td>
<td>Chapter 7 of ICI</td>
<td><a href="https://arxiv.org/abs/1906.02120" target="_blank"
rel="noopener noreferrer">Adapting Neural Networks for the Estimation of Treatment
Effects (Shi, Blei, Veitch, 2019)</a></td>
</tr>
<tr><td>7</td>
<td>Mar 16</td>
<td>Unobserved Confounding,<br />Bounds, and<br />Sensitivity Analysis</td>
<td><a href="https://www.youtube.com/watch?v=IXNMYqUsBBQ&list=PLoazKTcS0Rzb6bb9L508cyJ1z-U9iWkA0&index=47"
target="_blank" rel="noopener noreferrer">Video</a><br />
<a href="/slides/7%20-%20Unobserved%20Confounding.pdf" download="">Slides</a>
</td>
<td>Chapter 8 of ICI</td>
<td><a href="https://arxiv.org/abs/2003.01747" target="_blank"
rel="noopener noreferrer">Sense and Sensitivity Analysis: Simple Post-Hoc Analysis
of Bias Due to Unobserved Confounding (Veitch & Zaveri, 2020)</a></td>
</tr>
<tr><td>8</td>
<td>Mar 23</td>
<td>Instrumental Variables</td>
<td><a href="https://www.youtube.com/watch?v=Mco16tUSA-U&list=PLoazKTcS0Rzb6bb9L508cyJ1z-U9iWkA0&index=53"
target="_blank" rel="noopener noreferrer">Video</a><br />
<a href="/slides/8%20-%20Instrumental%20Variables.pdf" download="">Slides</a>
</td>
<td>Chapter 9 of ICI</td>
<td><a href="http://proceedings.mlr.press/v70/hartford17a.html" target="_blank"
rel="noopener noreferrer">Deep IV: A Flexible Approach for Counterfactual Prediction
(Hartford et al., 2017)</a></td>
</tr>
<tr><td>9</td>
<td>Mar 30</td>
<td>Difference-in-Differences<br /><a href="http://economics.mit.edu/faculty/abadie"
target="_blank" rel="noopener noreferrer">Alberto Abadie</a><br />Synthetic Control</td>
<td><a href="https://www.youtube.com/watch?v=tT8xLRS_cRQ&list=PLoazKTcS0Rzb6bb9L508cyJ1z-U9iWkA0&index=58"
target="_blank" rel="noopener noreferrer">Video</a><br />
<a href="/slides/9%20-%20Difference-in-Differences.pdf" download="">Slides</a><br />
<a href="https://www.youtube.com/watch?v=nKzNp-qpE-I&list=PLoazKTcS0RzZ1SUgeOgc6SWt51gfT80N0&index=11"
target="_blank" rel="noopener noreferrer">Guest Talk</a>
</td>
<td>Chapter 10 of ICI</td>
<td><a href="https://www.princeton.edu/~davidlee/wp/RDDEconomics.pdf" target="_blank"
rel="noopener noreferrer">Regression Discontinuity Designs in Economics (Lee &
Lemieux, 2010)</a></td>
</tr>
<tr><td>10</td>
<td>Apr 6</td>
<td>Causal Discovery from Observational Data<br /><a href="http://web.math.ku.dk/~peters/"
target="_blank" rel="noopener noreferrer">Jonas Peters</a></td>
<td><a href="https://www.youtube.com/watch?v=lVE-4deFe7c&list=PLoazKTcS0Rzb6bb9L508cyJ1z-U9iWkA0&index=62"
target="_blank" rel="noopener noreferrer">Video</a><br />
<a href="/slides/10%20-%20Causal%20Discovery%20from%20Observational%20Data.pdf"
download="">Slides</a><br />
</td>
<td>Chapter 11 of ICI</td>
<td><a href="https://www.nature.com/articles/s41467-019-10105-3" target="_blank"
rel="noopener noreferrer">Inferring causation from time series in Earth system
sciences (Runge et al., 2019)</a></td>
</tr>
<tr><td>11</td>
<td>Apr 13</td>
<td>Causal Discovery from Interventions</td>
<td><a href="https://www.youtube.com/watch?v=de2ODel8F1k&list=PLoazKTcS0Rzb6bb9L508cyJ1z-U9iWkA0&index=69"
target="_blank" rel="noopener noreferrer">Video</a><br />
<a href="/slides/11%20-%20Causal%20Discovery%20from%20Interventions.pdf"
download="">Slides</a><br />
</td>
<td>Chapter 12 of ICI<br />(Coming Soon?)</td>
<td><a href="https://arxiv.org/abs/1705.10220" target="_blank"
rel="noopener noreferrer">Permutation-based Causal Inference Algorithms with
Interventions (Wang et al., 2017)</a></td>
</tr>
<tr><td>12</td>
<td>Apr 20 </td>
<td>Transfer Learning<br />Transportability</td>
<td><a href="https://www.youtube.com/watch?v=JNq4oCV9C5k&list=PLoazKTcS0Rzb6bb9L508cyJ1z-U9iWkA0&index=77"
target="_blank" rel="noopener noreferrer">Video</a><br />
<a href="/slides/12%20-%20Transfer%20Learning%20and%20Transportability.pdf"
download="">Slides</a><br />
</td>
<td>Chapter 13 of ICI<br />(Coming Soon?)</td>
<td><a href="https://arxiv.org/abs/2006.07433" target="_blank" rel="noopener noreferrer">A
causal framework for distribution generalization (Christiansen et al., 2020)</a>
</td>
</tr>
<tr><td>13</td>
<td>Apr 27</td>
<td><a href="https://yoshuabengio.org/profile/" target="_blank"
rel="noopener noreferrer">Yoshua Bengio</a> <br />Causal Representation
Learning</td>
<td><a href="https://www.youtube.com/watch?v=rKZJ0TJWvTk&list=PLoazKTcS0Rzb6bb9L508cyJ1z-U9iWkA0&index=80"
target="_blank" rel="noopener noreferrer"></a><br />
<a href="/slides/Yoshua_Bengio_Guest_Talk_Towards_Causal_Representation_Learning.pdf"
download="">Slides</a><br />
</td>
<td>None</td>
<td><a href="https://arxiv.org/abs/1907.02893" target="_blank"
rel="noopener noreferrer">Invariant Risk Minimization (Arjovsky et al., 2019)</a>
</td>
</tr>
<tr><td>14</td>
<td>May 4</td>
<td>Counterfactuals<br />Mediation</td>
<td><a href="https://www.youtube.com/watch?v=f8PEpthLlN4&list=PLoazKTcS0Rzb6bb9L508cyJ1z-U9iWkA0&index=81"
target="_blank" rel="noopener noreferrer">Video</a><br />
<a href="/slides/14%20-%20Counterfactuals%20and%20Mediation.pdf"
download="">Slides</a><br />
</td>
<td>Chapter 14 of ICI<br>Coming Soon? </td>
<td><a href="https://ftp.cs.ucla.edu/pub/stat_ser/r321-ijcai05.pdf" target="_blank"
rel="noopener noreferrer">Identifiability of Path-Specific Effects (Avin, Shpitser,
& Pearl, 2005)</a></td>
</tr>
</tbody>
</table>
<h2 id="course-textbook">Course Textbook</h2>
<p>Draft of first 10 chapters (continually updated with new chapters throughout the course):</p>
<div style="text-align: center;">
<a href="Introduction_to_Causal_Inference-Dec17_2020-Neal.pdf">
<span style="font-size: 24px;">Introduction to Causal Inference (ICI)</span><br />
<span style="font-size: 18px;">from a Machine Learning Perspective</span>
</a>
</div>
<p>This is a book <em>draft</em>, so I greatly appreciate any feedback you're willing to send my way.
If you're unsure whether I'll be receptive to it or not, don't be.
Please send any feedback to me using the “Book†option of the <a
href="https://docs.google.com/forms/d/e/1FAIpQLSfoDk_PftCTD5aSqz7TP_MG8heIw0wSH4OVEkIsSvCaLgSsXw/viewform?usp=sf_link">feedback
form</a>.
Feedback can be at the word level, sentence level, section level, chapter level, etc.
Here's a non-exhaustive list of useful kinds of feedback:</p>
<ul>
<li>Typoz.</li>
<li>Some part is confusing.</li>
<li>You notice your mind start to wonder or don't feel motivated to read some part.</li>
<li>Some part seems like it can be cut.</li>
<li>You feel strongly that some part absolutely should not be cut.</li>
<li>Some parts are not connected well.</li>
<li>When moving from one part to the next, you notice that there isn't a natural flow.</li>
<li>A new active reading exercise you thought of.</li>
</ul>
<h2 id="prerequisites">Prerequisites</h2>
<p><strong>There is one main prerequisite: basic probability.</strong> This course assumes you've taken
an introduction to probability course at the undergraduate level or have had equivalent experience.
Topics from statistics and machine learning will pop up in the course from time to time, so some
familiarity with those will be helpful, but is not necessary.
For example, if cross-validation is a new concept to you, you can learn it relatively quickly at the
point in the course that it pops up.
And in Section 2.4 of the book, we give a primer on some statistics terminology that we'll use.</p>
<h2 id="faqs">FAQs</h2>
<p>Q: Where should I ask questions about a given lecture?<br />
A: Use the YouTube comment selection below the relevant video. I check it once per day on week days.
</p>
<p>Q: Is this course for credit?<br />
A: No.</p>
<p>Q: Is this course free?<br />
A: Yes!</p>
<p>Q: What time is the course?<br />
A: Only the guest talks will have specific times (listed in the schedule). The regular lecture
videos won't be live and will usually be uploaded to YouTube on Mondays.</p>
<p>Q: I'm not receiving course emails.<br />
A: Email me with “[Causal Course]†at the beginning of your email subject, and I'll fix it.</p>
<h2 id="feedback">Feedback</h2>
<p>If you have any feedback about the course to send my way, I welcome it!
Please send it <a
href="https://docs.google.com/forms/d/e/1FAIpQLSfoDk_PftCTD5aSqz7TP_MG8heIw0wSH4OVEkIsSvCaLgSsXw/viewform?usp=sf_link">here</a>.
You can include your name or not include your name.
Either works.</p>
<h2 id="potential-reading-group-papers-by-week">Potential Reading Group Papers by Week</h2>
<p>We will have a small weekly reading group that runs in parallel to the course.
Before any given week's reading group meeting, 1-3 people will have read the week's paper in detail
and already thought about discussion topics.
These 1-3 people will then lead a discussion of a small number of people who have all made
themselves familiar with the paper.
The discussion group will be kept small (at most 15) in order to facilitate quality discussion.
You can ensure that you have a place in the discussion group every week you'd like by signing up to
be a discussion leader for at least one week.
Below, I give a list of potential reading group papers, organized by week/topic, just like the
course schedule is.
You can email me at [email protected] to let me know that you'd like to lead a certain week's
discussion, which paper(s) you're considering, or to discuss other papers you'd like to discuss that
are not on the list.</p>
<ol>
<li>Motivation and Preview - No reading group</li>
<li>Potential Outcomes
<ul>
<li><a href="https://www.nature.com/articles/ijo200882">Does obesity shorten life? The
importance of well-defined interventions to answer causal questions (Hernán &
Taubman, 2008)</a></li>
<li><a href="https://ftp.cs.ucla.edu/pub/stat_ser/r483-reprint.pdf">Does Obesity Shorten
Life? Or is it the Soda? On Non-manipulable Causes (Pearl, 2018)</a></li>
</ul>
</li>
<li>Graphical Models and SCMs
<ul>
<li><a
href="https://www.degruyter.com/view/j/jci.2019.7.issue-1/jci-2019-2002/jci-2019-2002.xml">On
the Interpretation of do(x) (Pearl, 2019)</a></li>
<li><a href="https://arxiv.org/abs/1203.6502">Quantifying causal influences (Janzing et al.,
2012)</a></li>
<li><a href="https://ftp.cs.ucla.edu/pub/stat_ser/r391.pdf">Trygve Haavelmo and the
Emergence of Causal Calculus (Pearl, 2014)</a></li>
</ul>
</li>
<li>Randomized Experiments, Frontdoor Adjustment, and <em>do</em>-calculus
<ul>
<li><a
href="http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.644.1881&rep=rep1&type=pdf">Single
World Intervention Graphs: A Primer (Richardson & Robins, 2013)</a></li>
</ul>
<ul>
<li><a
href="http://marcfbellemare.com/wordpress/wp-content/uploads/2019/08/BellemareBloemFDCAugust2019.pdf">The
Paper of How: Estimating Treatment Effects Using the Front-Door Criterion (Bellemare
& Bloem, 2019)</a></li>
<li><a href="https://causalai.net/r60.pdf">On Pearl's Hierarchy and the Foundations of
Causal Inference (Bareinboim et al., 2020)</a></li>
</ul>
</li>
<li>Estimation and Conditional Average Treatment Effects
<ul>
<li><a href="https://arxiv.org/abs/1606.03976">Estimating individual treatment effect:
generalization bounds and algorithms (Shalit, Johansson, & Sontag, 2017)</a>
</li>
<li><a href="https://arxiv.org/abs/1906.02120">Adapting Neural Networks for the Estimation
of Treatment Effects (Shi, Blei, Veitch, 2019)</a></li>
<li><a href="https://arxiv.org/abs/1610.01271">Generalized Random Forests (Athey,
Tibshirani, Wager, 2019)</a></li>
<li><a href="https://arxiv.org/abs/1706.03461">Meta-learners for Estimating Heterogeneous
Treatment Effects using Machine Learning (Künzel et al., 2017)</a> (caution: not
about meta-learning in the ML sense)</li>
</ul>
</li>
<li>Sensitivity Analysis
<ul>
<li><a href="https://rss.onlinelibrary.wiley.com/doi/full/10.1111/rssb.12348">Making sense
of sensitivity: extending omitted variable bias (Cinelli & Hazlett, 2019)</a>
</li>
<li><a href="https://arxiv.org/abs/2003.01747">Sense and Sensitivity Analysis: Simple
Post-Hoc Analysis of Bias Due to Unobserved Confounding (Veitch & Zaveri,
2020)</a></li>
<li><a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3800481/">An Introduction to
Sensitivity Analysis for Unobserved Confounding in Non-Experimental Prevention
Research (Liu, Kuramoto, & Stuart, 2013)</a></li>
<li><a href="http://proceedings.mlr.press/v97/cinelli19a.html">Sensitivity Analysis of
Linear Structural Causal Models (Cinelli et al., 2019)</a></li>
</ul>
</li>
<li>Instrumental Variables, Regression Discontinuity, Difference-in-Differences, and Synthetic
Control
<ul>
<li><a
href="http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.883.6034&rep=rep1&type=pdf">Improving
Causal Inference: Strengths and Limitations of Natural Experiments (Dunning,
2007)</a></li>
<li><a href="http://paa2019.populationassociation.org/uploads/190202">Alternative Causal
Inference Methods in Population Health Research: Evaluating Tradeoffs and
Triangulating Evidence (Mattay et al., 2019)</a></li>
<li><a href="http://proceedings.mlr.press/v70/hartford17a/hartford17a.pdf">Deep IV: A
Flexible Approach for Counterfactual Prediction (Hartford et al., 2017)</a></li>
<li><a href="https://www.princeton.edu/~davidlee/wp/RDDEconomics.pdf">Regression
Discontinuity Designs in Economics (Lee & Lemieux, 2010)</a></li>
<li>Synthetic Controls (there are several different Abadie papers; message me, if you're
interested in this topic)</li>
</ul>
</li>
<li>Causal Discovery without Experiments
<ul>
<li><a href="https://www.nature.com/articles/s41467-019-10105-3">Inferring causation from
time series in Earth system sciences (Runge et al., 2019)</a></li>
<li><a href="https://jmlr.org/papers/v17/14-518.html">Distinguishing Cause from Effect Using
Observational Data: Methods and Benchmarks (Mooij et al., 2016)</a></li>
</ul>
<ul>
<li><a
href="https://www.cs.helsinki.fi/u/mjarvisa/papers/hyttinen-eberhardt-jarvisalo.uai15.pdf">Do-calculus
when the True Graph Is Unknown (Hyttinen, Eberhardt, Jarvisalo, 2015)</a></li>
<li><a href="https://www.frontiersin.org/articles/10.3389/fgene.2019.00524/full">Review of
Causal Discovery Methods Based on Graphical Models (Glymour, Zhang, & Spirtes,
2019)</a></li>
<li><a href="https://rss.onlinelibrary.wiley.com/doi/10.1111/rssb.12167">Causal inference by
using invariant prediction: identification and confidence intervals (Peters,
Bühlmann & Meinshausen, 2016)</a></li>
<li><a
href="https://papers.nips.cc/paper/3548-nonlinear-causal-discovery-with-additive-noise-models.pdf">Nonlinear
causal discovery with additive noise models (Hoyer et al., 2008)</a></li>
<li><a href="https://arxiv.org/abs/1903.01672">Causal Discovery from
Heterogeneous/Nonstationary Data with Independent Changes (Huang et al., 2020)</a>
</li>
</ul>
</li>
<li>Causal Discovery with Experiments
<ul>
<li><a href="https://jmlr.csail.mit.edu/papers/v14/hyttinen13a.html">Experiment Selection
for Causal Discovery (Hyttinen, Eberhardt, Hoyer, 2013)</a></li>
<li><a href="https://arxiv.org/abs/1104.2808">Characterization and Greedy Learning of
Interventional Markov Equivalence Classes of Directed Acyclic Graphs (Hauser &
Bühlmann, 2012)</a></li>
<li><a href="https://arxiv.org/abs/1802.06310">Characterizing and Learning Equivalence
Classes of Causal DAGs under Interventions (Yang, Katcoff, & Uhler, 2018)</a>
</li>
<li><a href="https://www.jmlr.org/papers/volume21/17-123/17-123.pdf">Joint Causal Inference
from Multiple Contexts (Mooij, Magliacane, & Claassen, 2020)</a></li>
</ul>
</li>
<li>Transportability and Transfer Learning
<ul>
<li><a href="https://ftp.cs.ucla.edu/pub/stat_ser/r400-reprint.pdf">External Validity: From
Do-Calculus to Transportability Across Populations (Pearl & Bareinboim,
2014)</a></li>
<li><a href="https://arxiv.org/abs/2006.07433">A causal framework for distribution
generalization (Christiansen et al., 2020)</a></li>
<li><a href="https://www.pnas.org/content/113/27/7345">Causal inference and the data-fusion
problem (Bareinboim & Pearl, 2016)</a></li>
<li><a href="https://icml.cc/2012/papers/625.pdf">On Causal and Anticausal Learning
(Schölkopf et al., 2012)</a></li>
<li><a href="http://proceedings.mlr.press/v28/zhang13d.html">Domain Adaptation under Target
and Conditional Shift (Zhang et al., 2013)</a></li>
<li><a href="https://mingming-gong.github.io/papers/AAAI_MULTI.pdf">Multi-Source Domain
Adaptation: A Causal View (Zhang, Gong, & Schölkopf., 2015)</a></li>
<li><a href="http://www.jmlr.org/papers/volume19/16-432/16-432.pdf">Invariant Models for
Causal Transfer Learning (Rojas-Carulla et al., 2016)</a></li>
<li><a href="https://arxiv.org/abs/2002.03278">Domain Adaptation As a Problem of Inference
on Graphical Models (Zhang et al., 2020)</a></li>
<li><a href="https://arxiv.org/abs/1707.06422">Domain Adaptation by Using Causal Inference
to Predict Invariant Conditional Distributions (Magliacane et al., 2018)</a></li>
</ul>
</li>
<li>Counterfactuals, Mediation, and Path-Specific Effects
<ul>
<li><a href="https://imai.fas.harvard.edu/research/files/mediation.pdf">Identification,
Inference and Sensitivity Analysis for Causal Mediation Effects (Imai, Keele, &
Yamamoto, 2010)</a></li>
<li><a href="https://ftp.cs.ucla.edu/pub/stat_ser/r321-ijcai05.pdf">Identifiability of
Path-Specific Effects (Avin, Shpitser, & Pearl, 2005)</a></li>
<li><a href="https://ftp.cs.ucla.edu/pub/stat_ser/r389.pdf">Interpretation and
Identification of Causal Mediation (Pearl, 2014)</a></li>
</ul>
</li>
<li>TBD - Overflow Week</li>
<li>Causal Representation Learning
<ul>
<li><a href="http://www.its.caltech.edu/~fehardt/papers/CPE_UAI2015.pdf">Visual Causal
Feature Learning (Chalupka, Perona, & Eberhardt, 2015)</a></li>
<li><a href="https://arxiv.org/abs/1605.08179">Discovering causal signals in images
(Lopez-Paz et al., 2017)</a></li>
<li><a href="https://arxiv.org/abs/1907.02893">Invariant Risk Minimization (Arjovsky et al.,
2019)</a></li>
</ul>
</li>
</ol>
</div>
</div>
</div>
<footer>
</footer>
</body>
</html>