double robust

0. Motivation Staggered‐adoption policies break the canonical two-period / two-group DiD model. It has been shown that the traditional two-way fixed-effects (TWFE) regression can assign negative weights to treatment effects, thereby obscuring their dynamic and heterogeneous patterns.

Introduction To estimate ATE, we can either use outcome regression or inverse propensity weighting (IPW). While each approach has merits, combining them offers significant advantage – double robustness. In this post, I summarize the intuition for doubly robust estimator from Professor Ding’s awesome textbook (Ding, 2024), and connects this framework to debiased machine learning (DML) through Riesz representation theory.

Debiased machine learning (DML) is a generic recipe. The idea behind it is adding a correction term to the plug-in estimator of the functional, which leads to properties such as semi-parametric efficiency, double robustness, and Neyman orthogonality.

In this post, I provide summary notes on the paper “Estimating Heterogeneous Treatment Effects with Right-Censored Data via Causal Survival Forests” by Cui et al. (2023). Motivation How to estimate heterogeneous treatment effects with right-censored data?

Introduction In this post, I will go over how causal forest works based on the tutorial in grf R package. Causal Forests offer a flexible, data-driven approach to estimating varied treatment effects, bridging machine learning and causal inference techniques.