econometrics

AIPW vs. Residual-on-Residual regression: Non-Parametric Flexibility or Efficiency?
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.
Jun 11, 2025 3 min read causal inference, econometrics
What is the Critical Radius in Riesz Regression?
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.
Jun 7, 2025 6 min read causal inference, econometrics
Notes on Propensity Score Methods
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.
Jun 3, 2025 6 min read causal inference, econometrics
Notes on DML for DiD: A Unified Approach
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.
Jun 2, 2025 3 min read causal inference, econometrics
Notes on causal inference with No Overlap – Regression Discontinuity
Here is my notes on regression discontinuity from Prof. Ding’s textbook (2024) and Prof. Imai’s lecture notes. Motivation We often cannot run a randomized experiment and have to use/design observational studies to find a setting where credible causal inference is possible.
May 31, 2025 2 min read causal inference, econometrics, quantitive-marketing
Notes on Control Function Method
Motivation Endogeneity: When an explanatory variable (treatment) is correlated with the unobserved error term (e.g., due to omitted variables, measurement error, or simultaneity). Consequence: Standard regression (e.g., OLS) yields biased estimates.
May 31, 2025 4 min read causal inference, econometrics, quantitive-marketing
Notes on IV Free Methods
Here’s a overview of instrument-free methods: Latent Instrumental Variable (LIV) Gaussian Copula (GC) The goal is for dealing with endogeneity when no external instruments are available. Motivation Finding good instruments is hard.
May 30, 2025 3 min read causal inference, quantitive-marketing, econometrics
Notes on Instrumental Variables
Here are my notes on instrumental variables from Stefan’s lecture materials. Motivation How can we identify causal effects W→Y when we are in the presence of unobserved confounding U?
May 29, 2025 6 min read causal inference, econometrics
Notes on Callaway & Sant’Anna (2021) – Staggered Adoption DiD
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.
May 27, 2025 7 min read causal inference, econometrics
Notes on Synthetic Control
Motivation 1. Data feature – only one treatment Treatment is assigned at an aggregate level, to group of individuals Example: policy interventions often take place at an aggregate level, and affect aggregate entities, such as schools, or geographic or administrative areas Only one or a few groups of treated units, and many more control units
May 22, 2025 4 min read causal inference, econometrics
Intuition for Doubly Robust Estimator
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.
May 18, 2025 4 min read causal inference, econometrics
Big Picture of Debiased Machine Learning
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.
Mar 25, 2025 2 min read causal inference, econometrics
Orthogonal vs Non-orthogonal Learning
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, βj=1/j2, (γDW)j=1/j2 and Y=1⋅D+β′W+ϵY
Feb 22, 2025 3 min read causal inference, econometrics
Notes on Semiparametric Models
Motivation Semiparametric models contain both a finite-dimensional parameter of interest (θ) and an infinite-dimensional nuisance parameter (η). The goal is to estimate θ as efficiently as possible while filtering out the impact of η.
Feb 5, 2025 2 min read Probability, econometrics, causal inference
From Donsker Classes to Neyman Orthogonality: The Power of DML
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.
Feb 20, 2025 2 min read causal inference, econometrics, Probability
Hausman-Taylor estimator Notes
Hausman-Taylor in R The following example is from here. Example: The fixed effects model, however, does not allow time-invariant variables such as educ or black. Since the problem of the
Mar 27, 2024 1 min read econometrics
A Review of Causal Inference Methods – Models and Comparison
last update: 06-05-2025 Introduction Establishing causation from observational data remains one of the most challenging tasks in empirical research. This guide examines twelve major causal inference methods, from traditional econometric approaches (DiD, IV, RD) to modern machine learning techniques (Causal Forests, DML, Auto-DML).
Jan 12, 2024 13 min read causal inference, econometrics, quantitive-marketing