doubly robust

Predicting outcomes with right-censored survival data forces a choice: do we model the outcome distribution, or the censoring mechanism? Classical transformations require us to commit to one. Rubin and van der Laan (2007) tell us we don’t have to.

Doubly Robust Inference via Calibration TL;DR Van der Laan, Luedtke, and Carone introduce “calibrated debiased machine learning” (calibrated DML), a method that achieves doubly robust asymptotic normality for causal inference estimators by simply adding an isotonic regression calibration step to standard DML pipelines.

TL;DR Cohn et al. (2023) introduces the balancing approach to weighting for causal inference in observational studies. Unlike traditional methods that model the propensity score directly, balancing weights are estimated by solving an optimization problem that directly targets covariate balance between treatment groups.

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.

Introduction Two powerful tools in causal inference are the Augmented Inverse Propensity Weighting (AIPW) estimator and the Residual-on-Residual regression estimator for partially linear models. Drawing from Wager’s notes (2024), this post breaks down how these estimators work, compares their strengths and weaknesses, and offers tips for when to use each.

Introduction Here are my notes on propensity scores, mainly from Prof. Ding’s textbook (2024). The traditional propensity score analysis workflow is shown in the image below, which I will not cover in detail.

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.

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 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.