Results 31 to 40 of about 117 (76)

Double machine learning and automated confounder selection: A cautionary tale

Journal of Causal Inference, 2023
Double machine learning (DML) has become an increasingly popular tool for automated variable selection in high-dimensional settings. Even though the ability to deal with a large number of potential covariates can render selection-on-observables ...
Hünermund Paul, Louw Beyers, Caspi Itamar   +2 more
doaj   +1 more source

Role of placebo samples in observational studies

Journal of Causal Inference
In an observational study, it is common to leverage known null effects to detect bias. One such strategy is to set aside a placebo sample – a subset of data immune from the hypothesized cause-and-effect relationship. Existence of an effect in the placebo
Ye Ting, He Qijia, Chen Shuxiao, Zhang Bo   +3 more
doaj   +1 more source

From urn models to box models: Making Neyman's (1923) insights accessible

Journal of Causal Inference
Neyman’s 1923 paper introduced the potential outcomes framework and the foundations of randomization-based inference. We discuss the influence of Neyman’s paper on four introductory to intermediate-level textbooks by Berkeley faculty members (Scheffé ...
Lin Winston, Dudoit Sandrine, Nolan Deborah, Speed Terence P.   +3 more
doaj   +1 more source

Doubly robust estimators for generalizing treatment effects on survival outcomes from randomized controlled trials to a target population

Journal of Causal Inference, 2022
In the presence of heterogeneity between the randomized controlled trial (RCT) participants and the target population, evaluating the treatment effect solely based on the RCT often leads to biased quantification of the real-world treatment effect.
Lee Dasom, Yang Shu, Wang Xiaofei
doaj   +1 more source

Conditional generative adversarial networks for individualized causal mediation analysis

Journal of Causal Inference
Most classical methods popularly used in causal mediation analysis can only estimate the average causal effects and are difficult to apply to precision medicine.
Huan Cheng, Sun Rongqian, Song Xinyuan
doaj   +1 more source

Sensitivity analysis for causal effects with generalized linear models

Journal of Causal Inference, 2022
Residual confounding is a common source of bias in observational studies. In this article, we build upon a series of sensitivity analyses methods for residual confounding developed by Brumback et al. and Chiba whose sensitivity parameters are constructed
Sjölander Arvid, Gabriel Erin E., Ciocănea-Teodorescu Iuliana   +2 more
doaj   +1 more source

The built-in selection bias of hazard ratios formalized using structural causal models [PDF]

It is known that the hazard ratio lacks a useful causal interpretation. Even for data from a randomized controlled trial, the hazard ratio suffers from so-called built-in selection bias as, over time, the individuals at risk among the exposed and ...
Post, Richard A.J., Putter, Hein, van den Heuvel, Edwin R.   +2 more
core   +1 more source

Prospective and retrospective causal inferences based on the potential outcome framework

Journal of Causal Inference
In this article, we discuss both prospective and retrospective causal inferences, building on Neyman’s potential outcome framework. For prospective causal inference, we review criteria for confounders and surrogates to avoid the Yule–Simpson paradox and ...
Geng Zhi, Zhang Chao, Wang Xueli, Liu Chunchen, Wei Shaojie   +4 more
doaj   +1 more source

The d-separation criterion in Categorical Probability

, 2022
The d-separation criterion detects the compatibility of a joint probability distribution with a directed acyclic graph through certain conditional independences.
Fritz, Tobias, Klingler, Andreas
core  

An optimal transport approach to estimating causal effects via nonlinear difference-in-differences

Journal of Causal Inference
We propose a nonlinear difference-in-differences (DiD) method to estimate multivariate counterfactual distributions in classical treatment and control study designs with observational data.
Torous William, Gunsilius Florian, Rigollet Philippe   +2 more
doaj   +1 more source