Journal of Causal Inference

2013 - 2026

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

From De Gruyter
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Volume 14, issue 1, 2026

Estimating the effects of staggered interventions with count and binary outcomes: a simulation study pp. 00

Yadav Anil, John McHale, Harold Jason and O’Neill Stephen

A causal framework for the self-controlled case series design pp. 14

Etiévant Lola, Gail Mitchell H. and Follmann Dean

Regression to the mean in regression discontinuity design: bias and sensitivity analysis pp. 15

Karmakar Bikram

Estimating average causal effects with incomplete exposure and confounders pp. 17

Wen Lan and McGee Glen

Estimation of time-varying treatment effects using marginal structural models dependent on partial treatment history pp. 17

Seya Nodoka, Taguri Masataka and Ishii Takeo

Bridging binarization: causal inference with dichotomized continuous exposures pp. 19

Lee Kaitlyn, Hubbard Alan and Schuler Alejandro

Causal generalized linear models via Pearson risk invariance pp. 19

Polinelli Alice, Vinciotti Veronica and Wit Ernst C.

The optimality of blocking designs in equally and unequally allocated randomized experiments with general response pp. 19

Azriel David, Krieger Abba M. and Kapelner Adam

Assessing surrogate heterogeneity in real world data using meta-learners pp. 20

Knowlton Rebecca and Parast Layla

Generalized coarsened confounding for causal effects: a large-sample framework pp. 21

Ghosh Debashis and Wang Lei

Untangling sample and population level estimands in Bayesian causal computation pp. 21

Oganisian Arman

Doubly-robust functional average treatment effect estimation pp. 22

Testa Lorenzo, Boschi Tobia, Chiaromonte Francesca, Kennedy Edward H. and Reimherr Matthew

A counterfactual analysis of the dishonest casino pp. 23

Haugh Martin B. and Singal Raghav

Discovery of critical thresholds in mixed exposures and estimation of policy intervention effects pp. 27

McCoy David B., Hubbard Alan, Mark van der Laan and Schuler Alejandro

Bayesian analysis of regression discontinuity designs with heterogeneous treatment effects pp. 27

Tao Kevin, Ruppert David and Wang Y. Samuel

A diagnostic to find and help combat stochastic positivity issues – with a focus on continuous treatments pp. 28

Ring Katharina and Schomaker Michael

Semiparametric discovery and estimation of interaction in mixed exposures using stochastic interventions pp. 29

McCoy David B., Hubbard Alan, Mark van der Laan and Schuler Alejandro

Do LLMs act as repositories of causal knowledge? pp. 30

Nick Huntington-Klein and Murray Eleanor J.

Adaptive-TMLE for the average treatment effect based on randomized controlled trial augmented with real-world data pp. 36

Mark van der Laan, Qiu Sky, Tarp Jens Magelund and Lars van der Laan

Bounding causal effects with an unknown mixture of informative and non-informative missingness pp. 42

Rubinstein Max, Agniel Denis, Han Larry, Horvitz-Lennon Marcela and Normand Sharon-Lise

Volume 13, issue 1, 2025

Orthogonal prediction of counterfactual outcomes pp. 00

Vansteelandt Stijn and Morzywołek Paweł

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

Adding covariates to bounds: what is the question? pp. 15

Jonzon Gustav, Gabriel Erin E., Sjölander Arvid and Sachs Michael C.

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.

Applying the Causal Roadmap to longitudinal national registry data in Denmark: A case study of second-line diabetes medication and dementia pp. 18

Nance Nerissa, Mertens Andrew, Gerds Thomas Alexander, Wang Zeyi, Torp-Pedersen Christian, Mark van der Laan, Kvist Kajsa, Lange Theis, Zareini Bochra and Petersen Maya L.

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

Variable importance for causal forests: breaking down the heterogeneity of treatment effects pp. 26

Bénard Clément and Josse Julie

Rate doubly robust estimation for weighted average treatment effects pp. 28

Wang Yiming, Liu Yi and Yang Shu

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

Experiment-selector cross-validated targeted maximum likelihood estimator for hybrid RCT-external data studies pp. 33

Dang Lauren Eyler, Tarp Jens Magelund, Abrahamsen Trine Julie, Kvist Kajsa, Buse John B., Petersen Maya and Mark van der Laan

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

Multivariate zero-inflated causal model for regional mobility restriction effects on consumer spending pp. 41

Hong Taekwon, Lu Wenbin, Yang Shu and Ghosh Pulak

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

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