Journal of Causal Inference

2013 - 2025

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

From De Gruyter
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Volume 11, issue 1, 2023

On the pitfalls of Gaussian likelihood scoring for causal discovery pp. 11

Schultheiss Christoph and Bühlmann Peter

Double machine learning and automated confounder selection: A cautionary tale pp. 12

Paul Hünermund, Louw Beyers and Itamar Caspi

Personalized decision making – A conceptual introduction pp. 13

Mueller Scott and Pearl Judea

Testing for treatment effect twice using internal and external controls in clinical trials pp. 13

Yi Yanyao, Zhang Ying, Du Yu and Ye Ting

Identification of in-sample positivity violations using regression trees: The PoRT algorithm pp. 13

Danelian Gabriel, Foucher Yohann, Léger Maxime, Le Borgne Florent and Chatton Arthur

On the dimensional indeterminacy of one-wave factor analysis under causal effects pp. 15

VanderWeele Tyler J. and Batty Charles J. K.

All models are wrong, but which are useful? Comparing parametric and nonparametric estimation of causal effects in finite samples pp. 15

Rudolph Kara E., Williams Nicholas T., Miles Caleb H., Antonelli Joseph and Diaz Ivan

Bounding the probabilities of benefit and harm through sensitivity parameters and proxies pp. 16

Peña Jose M.

Efficient and flexible mediation analysis with time-varying mediators, treatments, and confounders pp. 17

Díaz Iván, Williams Nicholas and Rudolph Kara E.

Attributable fraction and related measures: Conceptual relations in the counterfactual framework pp. 18

Suzuki Etsuji and Yamamoto Eiji

Robust inference for matching under rolling enrollment pp. 19

Glazer Amanda K. and Pimentel Samuel D.

Confidence in causal inference under structure uncertainty in linear causal models with equal variances pp. 21

Strieder David and Drton Mathias

Sensitivity analysis for causal decomposition analysis: Assessing robustness toward omitted variable bias pp. 23

Park Soojin, Kang Suyeon, Lee Chioun and Ma Shujie

Quantitative probing: Validating causal models with quantitative domain knowledge pp. 23

Grünbaum Daniel, Stern Maike L. and Lang Elmar W.

Heterogeneous interventional effects with multiple mediators: Semiparametric and nonparametric approaches pp. 23

Rubinstein Max, Branson Zach and Kennedy Edward H.

Randomization-based, Bayesian inference of causal effects pp. 25

Leavitt Thomas

2D score-based estimation of heterogeneous treatment effects pp. 25

Ye Steven Siwei, Chen Yanzhen and Padilla Oscar Hernan Madrid

Minimally capturing heterogeneous complier effect of endogenous treatment for any outcome variable pp. 25

Goeun Lee, Choi Jin-young and Myoung-jae Lee

Conditional average treatment effect estimation with marginally constrained models pp. 26

C. van Amsterdam Wouter A. and Ranganath Rajesh

Robust variance estimation and inference for causal effect estimation pp. 27

Tran Linh, Petersen Maya, Schwab Joshua and J. van der Laan Mark

Precise unbiased estimation in randomized experiments using auxiliary observational data pp. 27

Gagnon-Bartsch Johann A., Sales Adam C., Wu Edward, Botelho Anthony F., Erickson John A., Miratrix Luke W. and Heffernan Neil T.

Matched design for marginal causal effect on restricted mean survival time in observational studies pp. 28

Lin Zihan, Ni Ai and Lu Bo

Model-based regression adjustment with model-free covariates for network interference pp. 29

Han Kevin and Ugander Johan

Potential outcome and decision theoretic foundations for statistical causality pp. 30

Richardson Thomas S. and Robins James M.

Adaptive normalization for IPW estimation pp. 33

Khan Samir and Ugander Johan

Causality and independence in perfectly adapted dynamical systems pp. 35

Blom Tineke and Mooij Joris M.

Exploiting neighborhood interference with low-order interactions under unit randomized design pp. 36

Cortez-Rodriguez Mayleen, Eichhorn Matthew and Yu Christina Lee

Instrumental variable regression via kernel maximum moment loss pp. 42

Zhang Rui, Imaizumi Masaaki, Schölkopf Bernhard and Muandet Krikamol

Randomized graph cluster randomization pp. 53

Ugander Johan and Yin Hao

Volume 10, issue 1, 2022

Simple yet sharp sensitivity analysis for unmeasured confounding pp. 1-17

Peña Jose M.

Decomposition of the total effect for two mediators: A natural mediated interaction effect framework pp. 18-44

Gao Xin, Li Li and Luo Li

Causal inference with imperfect instrumental variables pp. 45-63

Miklin Nikolai, Gachechiladze Mariami, Moreno George and Chaves Rafael

A unifying causal framework for analyzing dataset shift-stable learning algorithms pp. 64-89

Subbaswamy Adarsh, Chen Bryant and Saria Suchi

The variance of causal effect estimators for binary v-structures pp. 90-105

Kuipers Jack and Moffa Giusi

Treatment effect optimisation in dynamic environments pp. 106-122

Berrevoets Jeroen, Verboven Sam and Verbeke Wouter

Optimal weighting for estimating generalized average treatment effects pp. 123-140

Kallus Nathan and Santacatterina Michele

Causal inference in AI education: A primer pp. 141-173

Forney Andrew and Mueller Scott

A note on efficient minimum cost adjustment sets in causal graphical models pp. 174-189

Smucler Ezequiel and Andrea Rotnitzky

Comment on: “Decision-theoretic foundations for statistical causality” pp. 190-196

Shpitser Ilya

Estimating marginal treatment effects under unobserved group heterogeneity pp. 197-216

Hoshino Tadao and Takahide Yanagi

Decision-theoretic foundations for statistical causality: Response to Shpitser pp. 217-220

Dawid Philip

Causation and decision: On Dawid’s “Decision theoretic foundation of statistical causality” pp. 221-226

Pearl Judea

Properties of restricted randomization with implications for experimental design pp. 227-245

Nordin Mattias and Schultzberg Mårten

Clarifying causal mediation analysis: Effect identification via three assumptions and five potential outcomes pp. 246-279

Nguyen Trang Quynh, Schmid Ian, Ogburn Elizabeth L. and Stuart Elizabeth A.

Identifying HIV sequences that escape antibody neutralization using random forests and collaborative targeted learning pp. 280-295

Jin Yutong and Benkeser David

Decision-theoretic foundations for statistical causality: Response to Pearl pp. 296-299

Dawid Philip

Estimating complier average causal effects for clustered RCTs when the treatment affects the service population pp. 300-334

Schochet Peter Z.

A generalized double robust Bayesian model averaging approach to causal effect estimation with application to the study of osteoporotic fractures pp. 335-371

Talbot Denis and Beaudoin Claudia

Causal effect on a target population: A sensitivity analysis to handle missing covariates pp. 372-414

Colnet Bénédicte, Josse Julie, Varoquaux Gaël and Scornet Erwan

Doubly robust estimators for generalizing treatment effects on survival outcomes from randomized controlled trials to a target population pp. 415-440

Lee Dasom, Yang Shu and Wang Xiaofei

Sensitivity analysis for causal effects with generalized linear models pp. 441-479

Sjölander Arvid, Gabriel Erin E. and Ciocănea-Teodorescu Iuliana

Individualized treatment rules under stochastic treatment cost constraints pp. 480-493

Qiu Hongxiang, Carone Marco and Luedtke Alex

A Lasso approach to covariate selection and average treatment effect estimation for clustered RCTs using design-based methods pp. 494-514

Schochet Peter Z.

Bias attenuation results for dichotomization of a continuous confounder pp. 515-526

Gabriel Erin E., Peña Jose M. and Sjölander Arvid