double ML

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.

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.

TL;DR In the paper Automatic Debiased Machine Learning via Riesz Regression by Chernozhukov et al. (2024), the critical radius is a key concept for understanding how accurately machine learning (ML) can estimate the Riesz representer—a function used to debias ML estimates of economic parameters like treatment effects.

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.

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.

The following exercise is from CausalAIBook Notebook. The goal is to illustrate the difference between Neyman orthogonal vs non-orthogonal approach. Setting We compare the performance of the naive and orthogonal methods in a computational experiment where \(p=n=100\), \(\beta_j = 1/j^2\), \((\gamma_{DW})_j = 1/j^2\) and \[Y = 1 \cdot D + \beta' W + \epsilon_Y\]

Motivation Semiparametric models contain both a finite-dimensional parameter of interest ($\theta$) and an infinite-dimensional nuisance parameter ($\eta$). The goal is to estimate $\theta$ as efficiently as possible while filtering out the impact of $\eta$.

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.

Motivation & Intuition In classical semiparametric theory, we want to estimate a low‑dimensional target parameter (say, a treatment effect) while controlling for high‑dimensional nuisance functions (like nonparametric regressions). However, in order to use the central limit theorem (CLT) and to characterize the asymptotic behavior, classical results require that the space of functions in which these nuisance functions lie is “small” in a technical sense.

Difference between Econometrics & Statistics What is the difference between Econometrics and Statistics? Professor Joshua Angrist (MIT) explains the difference 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.