1
Preface
2
Introduction
2.1
A lab experiment
2.2
Challenges
2.3
DAG (Directed Acyclic Graph)
2.4
A simulated DGP
2.5
Discussion
3
Why Regression?
3.1
The best-fit line
3.2
Linear Regression Specification
3.3
Law of iterated expectation
3.4
Error term
3.5
Decomposition
3.6
Estimation
3.7
Running a regression
3.8
Standard Errors
3.9
An exercise
4
Regression and Gradient Descent
5
Standard Errors
6
Logistic regression
7
Causal Inference
7.1
Potential Outcome Framework: Neyman-Rubin Causal Model
7.2
Average treatment effect (ATE)
7.3
RCT
7.4
Average treatment effect on the treated (ATT)
7.5
An estimation example
7.6
Unconfoundedness assumption
7.7
Discussion
7.8
Reference
8
IPW and AIPW
8.1
A simple example
8.2
Aggregated Estimator
8.3
Propensity score
8.4
Estimation of propensity score
8.5
Using cross-fitting to predict propensity score
8.6
Propensity score stratification
8.7
Inverse Probability Weighting (IPW)
8.8
Comparing IPW with Aggregated Estimate
8.9
AIPW and Estimation
8.10
Assessing Balance
8.11
Cross-fitting
9
Difference in Differences
9.1
A Quick Introduction
9.2
Set up
9.3
An example: Evaluating the impact of Medicaid expansion on uninsured rate
9.4
Naive estimator
9.5
Canonical Difference in Differences Framework
9.6
DiD in multi-period set up
9.7
Conditional Parallel Trend Assumption
9.8
Some concerns with controls
9.9
The
\(2 \times 2\)
Difference-in-Differences Estimate
9.10
Event study model
9.11
Two way fixed effect (TWFE) Revisited
9.12
Various ways of estimation
9.13
Multi Period, Multi Group and Variation in Treatment Timing
9.14
Problem with TWFE in Multiple Group with Treatment Timing Variation
9.15
What is TWFE Estimating when there is Treatment Timing Variation?
9.16
Assumptions governing TWFEDD estimate
9.17
How Does Treatment Effect Heterogeneity in Time Affect TWFE?
10
Causal Forest
10.1
Introduction
10.2
Summary of GRF
10.3
Motivation for Causal Forests
10.4
Causal Forest
10.5
An example of causal forest
11
Heterogeneous Treatment Effects
11.1
Some ways to estimate CATE
11.2
Estimation
11.3
Some Remarks and Questions
Causal Inference
9
Difference in Differences