difference in differences

TL;DR Athey, Imbens, Qu, and Viviano introduce the Triply RObust Panel (TROP) estimator, which combines unit weights, time weights, and a flexible low-rank factor model to estimate causal effects in panel data.

Here are my study notes on the matrix completion method for causal panel data models proposed by Athey et al. (2021). 1. Setup and Notation $Y(0),Y(1)$ are potential outcome matrices

Introduction This blog post explores how Double Machine Learning (DML) extends to conditional Difference-in-Differences (DiD), focusing on doubly robust estimators. The key insight is that conditional DiD can be understood through the lens of cross-sectional ATT estimation.

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.

Motivation Abadie et al. (2010) motivate the synthetic control method with a model that generalizes the difference-in-differences (fixed-effects) model commonly applied in the empirical social science literature by allowing the effect of unobserved confounding characteristics to vary over time.