American Causal Inference Conference (ACIC)
2022 Data Challenge

Inverse Probability Weighting Difference-in-Differences (IPWDID)
Komodo Health

Authors:

Yuqin Wei, Matthew Epland and Jingyuan (Hannah) Liu

Abstract

In this American Causal Inference Conference (ACIC) 2022 challenge submission, the canonical difference-in-differences (DID) estimator has been used with inverse probability weighting (IPW) and strong simplifying assumptions to produce a benchmark model of the sample average treatment effect on the treated (SATT). Despite the restrictive assumptions and simple model, satisfactory performance in both point estimate and confidence intervals was observed, ranking in the top half of the competition.

Paper

Published in Observational Studies, Volume 9, Issue 3, 2023, the 2022 ACIC special issue.

• Published Paper
• pdf

Prompt

2022 Challenge Site

Cloning the Repository

ssh

git clone [email protected]:mepland/acic_causality_challenge_2022.git

https

git clone https://github.com/mepland/acic_causality_challenge_2022.git

Installing Dependencies

It is recommended to work in a python virtual environment to avoid clashes with other installed software.

python -m venv ~/.venvs/causality
source ~/.venvs/causality/bin/activate
pip install -r requirements.txt