Journal of Causal Inference
2013 - 2025
Current editor(s): Elias Bareinboim, Jin Tian and Iván Díaz From De Gruyter Bibliographic data for series maintained by Peter Golla (). Access Statistics for this journal.
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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
- 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
- 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
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