Journal of Causal Inference

2013 - 2025

Current editor(s): Elias Bareinboim, Jin Tian and Iván Díaz

From De Gruyter
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Volume 13, issue 1, 2025

Bounds on the fixed effects estimand in the presence of heterogeneous assignment propensities pp. 7

Humphreys Macartan

Role of placebo samples in observational studies pp. 12

Ye Ting, He Qijia, Chen Shuxiao and Zhang Bo

Highly adaptive Lasso for estimation of heterogeneous treatment effects and treatment recommendation pp. 13

Nizam Sohail, Codi Allison, Rogawski McQuade Elizabeth and Benkeser David

Valid causal inference with unobserved confounding in high-dimensional settings pp. 15

Moosavi Niloofar, Gorbach Tetiana and Xavier de Luna

Matching estimators of causal effects in clustered observational studies pp. 16

Cui Can, Zhang Yunshu, Yang Shu, Reich Brian J. and Gill David A.

Conservative inference for counterfactuals pp. 17

Balakrishnan Sivaraman, Kennedy Edward and Wasserman Larry

Spillover detection for donor selection in synthetic control models pp. 17

O’Riordan Michael and Gilligan-Lee Ciarán M.

Single proxy synthetic control pp. 22

Park Chan and Tchetgen Tchetgen Eric J.

Optimal precision of coarse structural nested mean models to estimate the effect of initiating ART in early and acute HIV infection pp. 23

Lok Judith J.

Combining observational and experimental data for causal inference considering data privacy pp. 23

Mann Charlotte Z., Sales Adam C. and Gagnon-Bartsch Johann A.

Mediated probabilities of causation pp. 24

Rubinstein Max, Cuellar Maria and Malinsky Daniel

The necessity of construct and external validity for deductive causal inference pp. 25

Esterling Kevin M., Brady David and Schwitzgebel Eric

Ancestor regression in structural vector autoregressive models pp. 25

Schultheiss Christoph, Ulmer Markus and Bühlmann Peter

Recovery and inference of causal effects with sequential adjustment for confounding and attrition pp. 29

Johan de Aguas, Pensar Johan, Varnet Pérez Tomás and Biele Guido

Beyond conditional averages: Estimating the individual causal effect distribution pp. 29

Post Richard A. J. and R. van den Heuvel Edwin

Targeting mediating mechanisms of social disparities with an interventional effects framework, applied to the gender pay gap in Western Germany pp. 30

Didden Christiane

Treatment effect estimation with observational network data using machine learning pp. 36

Emmenegger Corinne, Spohn Meta-Lina, Elmer Timon and Bühlmann Peter

A clarification on the links between potential outcomes and do-interventions pp. 36

Lucas De Lara

Causal additive models with smooth backfitting pp. 37

Morville Asger B. and Park Byeong U.

Targeted maximum likelihood based estimation for longitudinal mediation analysis pp. 39

Wang Zeyi, Laan Lars van der, Petersen Maya, Gerds Thomas, Kvist Kajsa and Laan Mark van der

Minimax rates and adaptivity in combining experimental and observational data pp. 40

Chen Shuxiao, Li Sai, Zhang Bo and Ye Ting

Causal structure learning in directed, possibly cyclic, graphical models pp. 41

Semnani Pardis and Robeva Elina

Decision making, symmetry and structure: Justifying causal interventions pp. 47

Johnston David O., Ong Cheng Soon and Williamson Robert C.

Volume 12, issue 1, 2024

Comparison of open-source software for producing directed acyclic graphs pp. 10

Pitts Amy J. and Fowler Charlotte R.

Sharp bounds for causal effects based on Ding and VanderWeele's sensitivity parameters pp. 10

Sjölander Arvid

Potential outcomes and decision-theoretic foundations for statistical causality: Response to Richardson and Robins pp. 11

Dawid Philip

From urn models to box models: Making Neyman's (1923) insights accessible pp. 12

Lin Winston, Dudoit Sandrine, Nolan Deborah and Speed Terence P.

Prospective and retrospective causal inferences based on the potential outcome framework pp. 15

Geng Zhi, Zhang Chao, Wang Xueli, Liu Chunchen and Wei Shaojie

Estimation of network treatment effects with non-ignorable missing confounders pp. 16

Sun Zhaohan, Zhu Yeying and Dubin Joel A.

Direct, indirect, and interaction effects based on principal stratification with a binary mediator pp. 16

Myoung-jae Lee

Optimal allocation of sample size for randomization-based inference from 2K factorial designs pp. 18

Ravichandran Arun, Pashley Nicole E., Libgober Brian and Dasgupta Tirthankar

Design-based RCT estimators and central limit theorems for baseline subgroup and related analyses pp. 18

Schochet Peter Z.

Mediation analyses for the effect of antibodies in vaccination pp. 19

Fay Michael P. and Follmann Dean A.

Causal inference with textual data: A quasi-experimental design assessing the association between author metadata and acceptance among ICLR submissions from 2017 to 2022 pp. 20

Chen Chang, Zhang Jiayao, Ye Ting, Roth Dan and Zhang Bo

Quantifying the quality of configurational causal models pp. 20

Baumgartner Michael and Falk Christoph

Neyman meets causal machine learning: Experimental evaluation of individualized treatment rules pp. 20

Li Michael Lingzhi and Imai Kosuke

Detecting treatment interference under K-nearest-neighbors interference pp. 20

Alzubaidi Samirah H. and Higgins Michael J.

Regression(s) discontinuity: Using bootstrap aggregation to yield estimates of RD treatment effects pp. 21

Mark Long and Rooklyn Jordan

Current philosophical perspectives on drug approval in the real world pp. 21

Landes Jürgen and Auker-Howlett Daniel J.

Energy balancing of covariate distributions pp. 22

Huling Jared D. and Mak Simon

Conditional generative adversarial networks for individualized causal mediation analysis pp. 23

Huan Cheng, Sun Rongqian and Song Xinyuan

Improved sensitivity bounds for mediation under unmeasured mediator–outcome confounding pp. 24

Sjölander Arvid and Waernbaum Ingeborg

Evaluating Boolean relationships in Configurational Comparative Methods pp. 25

Luna De Souter

Doubly weighted M-estimation for nonrandom assignment and missing outcomes pp. 25

Negi Akanksha

An optimal transport approach to estimating causal effects via nonlinear difference-in-differences pp. 26

Torous William, Gunsilius Florian and Rigollet Philippe

Double machine learning and design in batch adaptive experiments pp. 27

Li Harrison H. and Owen Art B.

A phenomenological account for causality in terms of elementary actions pp. 28

Janzing Dominik and Mejia Sergio Hernan Garrido

An approach to nonparametric inference on the causal dose–response function pp. 28

Hudson Aaron, Geng Elvin H., Odeny Thomas A., Bukusi Elizabeth A., Petersen Maya L. and J. van der Laan Mark

Some theoretical foundations for the design and analysis of randomized experiments pp. 30

Shi Lei and Li Xinran

The functional average treatment effect pp. 30

Sparkes Shane, Garcia Erika and Zhang Lu

Foundations of causal discovery on groups of variables pp. 32

Wahl Jonas, Ninad Urmi and Runge Jakob

Bias formulas for violations of proximal identification assumptions in a linear structural equation model pp. 34

Cobzaru Raluca, Welsch Roy, Finkelstein Stan, Ng Kenney and Shahn Zach

Nonparametric estimation of conditional incremental effects pp. 42

McClean Alec, Branson Zach and Kennedy Edward H.

Interactive identification of individuals with positive treatment effect while controlling false discoveries pp. 43

Duan Boyan, Wasserman Larry and Ramdas Aaditya