Machine learning for causal inference: on the use of cross-fit
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Machine learning for causal inference: on the use of cross-fit estimators

21 April 2020

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Papers citing "Machine learning for causal inference: on the use of cross-fit estimators"

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Title
Generalized coarsened confounding for causal effects: a large-sample framework Debashis Ghosh Lei Wang CML 57 0 0 06 Jan 2025
Performance of Cross-Validated Targeted Maximum Likelihood Estimation Matthew J. Smith Rachael V. Phillips C. Maringe Miguel Angel Luque-Fernandez 49 1 0 17 Sep 2024
Graph Machine Learning based Doubly Robust Estimator for Network Causal Effects Seyedeh Baharan Khatami Harsh Parikh Haowei Chen Sudeepa Roy Babak Salimi OOD 78 1 0 17 Mar 2024
Efficient adjustment for complex covariates: Gaining efficiency with DOPE Alexander Mangulad Christgau Niels Richard Hansen 93 3 0 20 Feb 2024
CausalMetaR: An R package for performing causally interpretable meta-analyses Guanbo Wang Sean McGrath Yi Lian CML 94 1 0 06 Feb 2024
Application of targeted maximum likelihood estimation in public health and epidemiological studies: a systematic review Matthew J. Smith Rachael V. Phillips M. Luque-Fernández C. Maringe OOD 56 22 0 13 Mar 2023
Meta-Learners for Estimation of Causal Effects: Finite Sample Cross-Fit Performance Gabriel Okasa CML 55 6 0 30 Jan 2022
Causal Bias Quantification for Continuous Treatments Gianluca Detommaso Michael Bruckner Philip Schulz Victor Chernozhukov CML 91 0 0 17 Jun 2021
Tutorial: Introduction to computational causal inference using reproducible Stata, R and Python code Matthew J. Smith C. Maringe B. Rachet M. Mansournia P. Zivich Stephen R. Cole M. Luque-Fernández CML 17 2 0 17 Dec 2020