causal machine learning

Motivation In empirical research, one of the challenges to causal inference is the potential presence of unobserved confounding. Even when we adjust for a wide range of observed covariates, there’s often a lingering concern: what if there are important variables we’ve failed to measure or include?

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.

Statistical Setting We observe data $(X_i, Y_i, W_i) \in \mathcal{X} \times \mathbb{R} \times {0,1}$ according to the potential outcomes model and we assume the following: (SUTVA) ${Y_i(0), Y_i(1)} \perp W_i

Difference between Econometrics & Statistics What is the difference between Econometrics and Statistics? Professor Joshua Angrist (MIT) explains the differnce in this video. Statisticians use sampling to make statistical inferences about large populations.

Here are some study notes for Double Machine Learning in causal inference. Introduction Double machine learning, as introduced by (Chernozhukov et al. 2018), is a methodology used in causal inference, which is particularly useful when dealing with high-dimensional data.