Robust Causal Inference using Double/Debiased Machine Learning

Double Machine Learning for causal inference | by Borja Velasco

Double Machine Learning makes the connection between these two points, taking inspiration and useful results from the second, for doing causal inference with ...

Evaluating Causal Machine Learning Methods

approach to estimate ATE is Double/Debiased Machine Learning (DML) introduced by Chernozhukov ... the outcome with the double robust estimator by Robins and ...

Double Machine Learning at Scale to Predict Causal Impact of ...

We also find that the mean of HTT values is robust with respect to the choice for number of clusters. ... Causal Inference Using Potential Outcomes. 2005. J. Amer ...

Machine learning in causal inference for epidemiology

TMLE is a doubly-robust, maximum-likelihood–based estimation method, developed by van der Laan and Rubin [31]. In addition to the initial ...

ddml: Double/debiased machine learning in Stata

A rich and growing literature exploits machine learning to facilitate causal inference. A central focus: high-dimensional controls and/or ...

Double Robust, Flexible Adjustment Methods for Causal Inference

Methods covered include Augmented Inverse Probability Weighting (AIPW), Targeted Maximum Likelihood. Estimation (TMLE), and Double/Debiased Machine Learning ( ...

Causal ML Book

Applied Causal Inference Powered by ML and AI ... An introduction to the emerging fusion of machine learning and causal inference. The book introduces ideas from ...

RieszNet and ForestRiesz: Automatic Debiased Machine Learning ...

inference on average causal effects using machine learning. Early literature ... and Robins, J. M. Doubly robust estimation in missing data and causal inference ...

1. The basics of double/debiased machine learning

with covariates x i ∼ N ( 0 , Σ ) , where Σ is a matrix with entries Σ k j = 0.7 | j − k | . We are interested in performing valid inference on the causal ...

awesome-causal-inference/src/academic-research.md at main

Meta-learners for Estimating Heterogeneous Treatment Effects using Machine Learning - Künzel, Sekhon, Bickel, Yu (2017). Double/Debiased Machine Learning for ...

Improved Unconventional Development Decisions using Causal ...

Then, the results for Double Machine Learning (DML), Doubly. Robust Learning ... Double/debiased machine learning for treatment and causal parameters. arXiv ...

Double machine learning for treatment and causal parameters

Such double ML estimators achieve the fastest rates of convergence and exhibit robust good behavior with respect to a broader class of probability distributions ...

STA 640 — Causal Inference Chapter 3.5. Doubly Robust Estimation

▷ The goal is to gain efficiency by using an outcome model but augment it by ... Double/debiased machine learning for treatment and structural parameters.

The Value Added of Machine Learning to Causal Inference - Index of /

the causal forest without clustering with the cluster-robust version. ... Double debiased machine learning nonparametric inference with continuous treatments.

DEMA: Enhancing Causal Analysis through Data Enrichment and ...

Our iterative pipeline addresses high-dimensional covariates, missing data, and incomplete joins using Double Machine Learning to control for con- founding ...

Evaluating (weighted) dynamic treatment effects by double machine ...

Bodory, The causalweight package for causal inference in R · Colangelo, Double debiased machine learning nonparametric inference with continuous treatments ...

Double debiased machine learning nonparametric inference with ...

This work gives tractable primitive conditions under which the nuisance estimators do not affect the first-order large sample distribution ...

Double Machine Learning Density Estimation for Local Treatment ...

Double debiased machine learning nonparametric inference ... Robust causal inference with continuous instruments using the local instrumental variable curve.

Double/Debiased Machine Learning for Dynamic

Robins. Doubly robust estimation in missing data and causal inference models. Biometrics, 61(4):962–973, 2005.

Recent Developments in Causal Inference and Machine Learning

The incorporation of machine learning in causal inference enables researchers to better address potential biases in estimating causal effects ...