The costs and benefits of uniformly valid causal inference with high-dimensional nuisance parameters

DOI10.1214/21-STS843WikidataQ130416845 ScholiaQ130416845MaRDI QIDQ2684684

Xavier de Luna, Jenny Häggström, Niloofar Moosavi

Publication date: 16 February 2023

Published in: Statistical Science (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2105.02071

zbMATH Keywords

regularizationmachine learningsuperefficiencydouble robustnesspost-model selection inference

Mathematics Subject Classification ID

Statistics (62-XX)

Uses Software

• R
• tmle
• hdm

Cites Work

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• Doubly Robust Estimation in Missing Data and Causal Inference Models
• Model-assisted inference for treatment effects using regularized calibrated estimation with high-dimensional data
• On asymptotically optimal confidence regions and tests for high-dimensional models
• Sparse estimators and the oracle property, or the return of Hodges' estimator
• Exact post-selection inference, with application to the Lasso
• Valid post-selection inference
• Comment: Understanding OR, PS and DR
• Robust inference on average treatment effects with possibly more covariates than observations
• On adaptive estimation
• On the application of probability theory to agricultural experiments. Essay on principles. Section 9. Translated from the Polish and edited by D. M. Dąbrowska and T. P. Speed
• Semiparametric theory and missing data.
• Overlap in observational studies with high-dimensional covariates
• Sparse Models and Methods for Optimal Instruments With an Application to Eminent Domain
• Confidence Intervals and Hypothesis Testing for High-Dimensional Regression
• Covariate selection for the nonparametric estimation of an average treatment effect
• Higher order influence functions and minimax estimation of nonlinear functionals
• Estimation of Regression Coefficients When Some Regressors Are Not Always Observed
• Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties
• On the Role of the Propensity Score in Efficient Semiparametric Estimation of Average Treatment Effects
• Adjusting for Nonignorable Drop-Out Using Semiparametric Nonresponse Models
• Outcome‐adaptive lasso: Variable selection for causal inference
• Inference in Linear Regression Models with Many Covariates and Heteroscedasticity
• Inference on Treatment Effects after Selection among High-Dimensional Controls
• Approximate Residual Balancing: Debiased Inference of Average Treatment Effects in High Dimensions
• Deep Neural Networks for Estimation and Inference
• Two-Step Estimation and Inference with Possibly Many Included Covariates
• Inference for treatment effect parameters in potentially misspecified high-dimensional models
• Debiased machine learning of conditional average treatment effects and other causal functions
• Double/debiased machine learning for treatment and structural parameters
• Robust estimation of causal effects via a high-dimensional covariate balancing propensity score
• Causal inference accounting for unobserved confounding after outcome regression and doubly robust estimation
• MODEL SELECTION AND INFERENCE: FACTS AND FICTION
• Confidence Intervals for Low Dimensional Parameters in High Dimensional Linear Models

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