Semiparametric estimation for average causal effects using propensity score-based spline

DOI10.1016/j.jspi.2020.10.004zbMath1465.62169OpenAlexW3106381340MaRDI QIDQ830691

Bo Lu, Qing Jiang, Peng Wu, Xinyi Xu, Xing-wei Tong

Publication date: 7 May 2021

Published in: Journal of Statistical Planning and Inference (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.jspi.2020.10.004

zbMATH Keywords

splinesemiparametric estimationpropensity scoreaverage causal effectheterogeneous variance

Mathematics Subject Classification ID

Density estimation (62G07) Numerical computation using splines (65D07) Applications of statistics to biology and medical sciences; meta analysis (62P10)

Related Items (1)

Doubly robust estimation in causal inference with missing outcomes: with an application to the aerobics center longitudinal study

Uses Software

• ElemStatLearn
• grf

Cites Work

• Unnamed Item
• Unnamed Item
• Doubly Robust Estimation in Missing Data and Causal Inference Models
• Demystifying double robustness: a comparison of alternative strategies for estimating a population mean from incomplete data
• Comment: Understanding OR, PS and DR
• Generalized random forests
• Convergence rates for parametric components in a partly linear model
• Generalized additive models
• Asymptotic theory of nonlinear least squares estimation
• Robust regression: Asymptotics, conjectures and Monte Carlo
• Statistical predictor identification
• A Distributional Approach for Causal Inference Using Propensity Scores
• The central role of the propensity score in observational studies for causal effects
• Estimation of Regression Coefficients When Some Regressors Are Not Always Observed
• On the Role of the Propensity Score in Efficient Semiparametric Estimation of Average Treatment Effects
• Estimation and Inference of Heterogeneous Treatment Effects using Random Forests
• Doubly robust estimation of partially linear models for longitudinal data with dropouts and measurement error in covariates
• Balancing Covariates via Propensity Score Weighting
• Estimating Exposure Effects by Modelling the Expectation of Exposure Conditional on Confounders
• Double/debiased machine learning for treatment and structural parameters
• Causal Inference for Statistics, Social, and Biomedical Sciences
• Simple least squares estimator for treatment effects using propensity score residuals
• Calibrating Sensitivity Analyses to Observed Covariates in Observational Studies
• Efficient Estimation of Average Treatment Effects Using the Estimated Propensity Score
• A Generalization of Sampling Without Replacement From a Finite Universe
• Observational studies.
• Comment: Demystifying double robustness: a comparison of alternative strategies for estimating a population mean from incomplete data
• A new look at the statistical model identification

This page was built for publication: Semiparametric estimation for average causal effects using propensity score-based spline