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2024, Volume 12, Issue 1
- 1-10 Sharp bounds for causal effects based on Ding and VanderWeele's sensitivity parameters
by Sjölander Arvid - 1-11 Potential outcomes and decision-theoretic foundations for statistical causality: Response to Richardson and Robins
by Dawid Philip - 1-12 From urn models to box models: Making Neyman's (1923) insights accessible
by Lin Winston & Dudoit Sandrine & Nolan Deborah & Speed Terence P. - 1-15 Prospective and retrospective causal inferences based on the potential outcome framework
by Geng Zhi & Zhang Chao & Wang Xueli & Liu Chunchen & Wei Shaojie - 1-16 Direct, indirect, and interaction effects based on principal stratification with a binary mediator
by Lee Myoung-jae - 1-16 Estimation of network treatment effects with non-ignorable missing confounders
by Sun Zhaohan & Zhu Yeying & Dubin Joel A. - 1-18 Design-based RCT estimators and central limit theorems for baseline subgroup and related analyses
by Schochet Peter Z. - 1-20 Detecting treatment interference under K-nearest-neighbors interference
by Alzubaidi Samirah H. & Higgins Michael J. - 1-20 Quantifying the quality of configurational causal models
by Baumgartner Michael & Falk Christoph - 1-20 Neyman meets causal machine learning: Experimental evaluation of individualized treatment rules
by Li Michael Lingzhi & Imai Kosuke - 1-20 Causal inference with textual data: A quasi-experimental design assessing the association between author metadata and acceptance among ICLR submissions from 2017 to 2022
by Chen Chang & Zhang Jiayao & Ye Ting & Roth Dan & Zhang Bo - 1-21 Current philosophical perspectives on drug approval in the real world
by Landes Jürgen & Auker-Howlett Daniel J. - 1-24 Improved sensitivity bounds for mediation under unmeasured mediator–outcome confounding
by Sjölander Arvid & Waernbaum Ingeborg - 1-25 Doubly weighted M-estimation for nonrandom assignment and missing outcomes
by Negi Akanksha - 1-26 An optimal transport approach to estimating causal effects via nonlinear difference-in-differences
by Torous William & Gunsilius Florian & Rigollet Philippe - 1-27 Double machine learning and design in batch adaptive experiments
by Li Harrison H. & Owen Art B. - 1-30 The functional average treatment effect
by Sparkes Shane & Garcia Erika & Zhang Lu - 1-30 Some theoretical foundations for the design and analysis of randomized experiments
by Shi Lei & Li Xinran - 1-32 Foundations of causal discovery on groups of variables
by Wahl Jonas & Ninad Urmi & Runge Jakob - 1-34 Bias formulas for violations of proximal identification assumptions in a linear structural equation model
by Cobzaru Raluca & Welsch Roy & Finkelstein Stan & Ng Kenney & Shahn Zach - 1-43 Interactive identification of individuals with positive treatment effect while controlling false discoveries
by Duan Boyan & Wasserman Larry & Ramdas Aaditya
January 2024, Volume 12, Issue 1
- 1-10 Comparison of open-source software for producing directed acyclic graphs
by Pitts Amy J. & Fowler Charlotte R. - 1-18 Optimal allocation of sample size for randomization-based inference from 2K factorial designs
by Ravichandran Arun & Pashley Nicole E. & Libgober Brian & Dasgupta Tirthankar - 1-19 Mediation analyses for the effect of antibodies in vaccination
by Fay Michael P. & Follmann Dean A. - 1-21 Regression(s) discontinuity: Using bootstrap aggregation to yield estimates of RD treatment effects
by Long Mark C. & Rooklyn Jordan - 1-22 Energy balancing of covariate distributions
by Huling Jared D. & Mak Simon - 1-23 Conditional generative adversarial networks for individualized causal mediation analysis
by Huan Cheng & Sun Rongqian & Song Xinyuan - 1-25 Evaluating Boolean relationships in Configurational Comparative Methods
by De Souter Luna - 1-28 A phenomenological account for causality in terms of elementary actions
by Janzing Dominik & Mejia Sergio Hernan Garrido - 1-42 Nonparametric estimation of conditional incremental effects
by McClean Alec & Branson Zach & Kennedy Edward H.
January 2023, Volume 11, Issue 1
- 1-11 On the pitfalls of Gaussian likelihood scoring for causal discovery
by Schultheiss Christoph & Bühlmann Peter - 1-12 Double machine learning and automated confounder selection: A cautionary tale
by Hünermund Paul & Louw Beyers & Caspi Itamar - 1-13 Personalized decision making – A conceptual introduction
by Mueller Scott & Pearl Judea - 1-15 On the dimensional indeterminacy of one-wave factor analysis under causal effects
by VanderWeele Tyler J. & Batty Charles J. K. - 1-16 Bounding the probabilities of benefit and harm through sensitivity parameters and proxies
by Peña Jose M. - 1-17 Efficient and flexible mediation analysis with time-varying mediators, treatments, and confounders
by Díaz Iván & Williams Nicholas & Rudolph Kara E. - 1-18 Attributable fraction and related measures: Conceptual relations in the counterfactual framework
by Suzuki Etsuji & Yamamoto Eiji - 1-19 Robust inference for matching under rolling enrollment
by Glazer Amanda K. & Pimentel Samuel D. - 1-21 Confidence in causal inference under structure uncertainty in linear causal models with equal variances
by Strieder David & Drton Mathias - 1-23 Sensitivity analysis for causal decomposition analysis: Assessing robustness toward omitted variable bias
by Park Soojin & Kang Suyeon & Lee Chioun & Ma Shujie - 1-25 Randomization-based, Bayesian inference of causal effects
by Leavitt Thomas - 1-26 Conditional average treatment effect estimation with marginally constrained models
by van Amsterdam Wouter A. C. & Ranganath Rajesh - 1-27 Robust variance estimation and inference for causal effect estimation
by Tran Linh & Petersen Maya & Schwab Joshua & van der Laan Mark J. - 1-28 Matched design for marginal causal effect on restricted mean survival time in observational studies
by Lin Zihan & Ni Ai & Lu Bo - 1-29 Model-based regression adjustment with model-free covariates for network interference
by Han Kevin & Ugander Johan - 1-30 Potential outcome and decision theoretic foundations for statistical causality
by Richardson Thomas S. & Robins James M. - 1-33 Adaptive normalization for IPW estimation
by Khan Samir & Ugander Johan - 1-35 Causality and independence in perfectly adapted dynamical systems
by Blom Tineke & Mooij Joris M. - 1-36 Exploiting neighborhood interference with low-order interactions under unit randomized design
by Cortez-Rodriguez Mayleen & Eichhorn Matthew & Yu Christina Lee - 1-42 Instrumental variable regression via kernel maximum moment loss
by Zhang Rui & Imaizumi Masaaki & Schölkopf Bernhard & Muandet Krikamol - 1-53 Randomized graph cluster randomization
by Ugander Johan & Yin Hao
2023, Volume 11, Issue 1
- 1-13 Testing for treatment effect twice using internal and external controls in clinical trials
by Yi Yanyao & Zhang Ying & Du Yu & Ye Ting - 1-13 Identification of in-sample positivity violations using regression trees: The PoRT algorithm
by Danelian Gabriel & Foucher Yohann & Léger Maxime & Le Borgne Florent & Chatton Arthur - 1-15 All models are wrong, but which are useful? Comparing parametric and nonparametric estimation of causal effects in finite samples
by Rudolph Kara E. & Williams Nicholas T. & Miles Caleb H. & Antonelli Joseph & Diaz Ivan - 1-23 Quantitative probing: Validating causal models with quantitative domain knowledge
by Grünbaum Daniel & Stern Maike L. & Lang Elmar W. - 1-23 Heterogeneous interventional effects with multiple mediators: Semiparametric and nonparametric approaches
by Rubinstein Max & Branson Zach & Kennedy Edward H. - 1-25 2D score-based estimation of heterogeneous treatment effects
by Ye Steven Siwei & Chen Yanzhen & Padilla Oscar Hernan Madrid - 1-25 Minimally capturing heterogeneous complier effect of endogenous treatment for any outcome variable
by Lee Goeun & Choi Jin-young & Lee Myoung-jae - 1-27 Precise unbiased estimation in randomized experiments using auxiliary observational data
by Gagnon-Bartsch Johann A. & Sales Adam C. & Wu Edward & Botelho Anthony F. & Erickson John A. & Miratrix Luke W. & Heffernan Neil T.
January 2022, Volume 10, Issue 1
- 1-17 Simple yet sharp sensitivity analysis for unmeasured confounding
by Peña Jose M. - 18-44 Decomposition of the total effect for two mediators: A natural mediated interaction effect framework
by Gao Xin & Li Li & Luo Li - 45-63 Causal inference with imperfect instrumental variables
by Miklin Nikolai & Gachechiladze Mariami & Moreno George & Chaves Rafael - 64-89 A unifying causal framework for analyzing dataset shift-stable learning algorithms
by Subbaswamy Adarsh & Chen Bryant & Saria Suchi - 90-105 The variance of causal effect estimators for binary v-structures
by Kuipers Jack & Moffa Giusi - 106-122 Treatment effect optimisation in dynamic environments
by Berrevoets Jeroen & Verboven Sam & Verbeke Wouter - 123-140 Optimal weighting for estimating generalized average treatment effects
by Kallus Nathan & Santacatterina Michele - 141-173 Causal inference in AI education: A primer
by Forney Andrew & Mueller Scott - 174-189 A note on efficient minimum cost adjustment sets in causal graphical models
by Smucler Ezequiel & Rotnitzky Andrea - 190-196 Comment on: “Decision-theoretic foundations for statistical causality”
by Shpitser Ilya - 197-216 Estimating marginal treatment effects under unobserved group heterogeneity
by Hoshino Tadao & Yanagi Takahide - 217-220 Decision-theoretic foundations for statistical causality: Response to Shpitser
by Dawid Philip - 221-226 Causation and decision: On Dawid’s “Decision theoretic foundation of statistical causality”
by Pearl Judea - 227-245 Properties of restricted randomization with implications for experimental design
by Nordin Mattias & Schultzberg Mårten - 246-279 Clarifying causal mediation analysis: Effect identification via three assumptions and five potential outcomes
by Nguyen Trang Quynh & Schmid Ian & Ogburn Elizabeth L. & Stuart Elizabeth A. - 280-295 Identifying HIV sequences that escape antibody neutralization using random forests and collaborative targeted learning
by Jin Yutong & Benkeser David - 296-299 Decision-theoretic foundations for statistical causality: Response to Pearl
by Dawid Philip - 300-334 Estimating complier average causal effects for clustered RCTs when the treatment affects the service population
by Schochet Peter Z. - 335-371 A generalized double robust Bayesian model averaging approach to causal effect estimation with application to the study of osteoporotic fractures
by Talbot Denis & Beaudoin Claudia - 372-414 Causal effect on a target population: A sensitivity analysis to handle missing covariates
by Colnet Bénédicte & Josse Julie & Varoquaux Gaël & Scornet Erwan - 415-440 Doubly robust estimators for generalizing treatment effects on survival outcomes from randomized controlled trials to a target population
by Lee Dasom & Yang Shu & Wang Xiaofei - 441-479 Sensitivity analysis for causal effects with generalized linear models
by Sjölander Arvid & Gabriel Erin E. & Ciocănea-Teodorescu Iuliana - 480-493 Individualized treatment rules under stochastic treatment cost constraints
by Qiu Hongxiang & Carone Marco & Luedtke Alex - 494-514 A Lasso approach to covariate selection and average treatment effect estimation for clustered RCTs using design-based methods
by Schochet Peter Z. - 515-526 Bias attenuation results for dichotomization of a continuous confounder
by Gabriel Erin E. & Peña Jose M. & Sjölander Arvid
January 2021, Volume 9, Issue 1
- 1-8 Two seemingly paradoxical results in linear models: the variance inflation factor and the analysis of covariance
by Ding Peng - 9-38 Identification of causal intervention effects under contagion
by Cai Xiaoxuan & Loh Wen Wei & Crawford Forrest W. - 39-77 Decision-theoretic foundations for statistical causality
by Dawid Philip - 78-82 Radical empiricism and machine learning research
by Pearl Judea - 83-108 A fundamental measure of treatment effect heterogeneity
by Levy Jonathan & van der Laan Mark & Hubbard Alan & Pirracchio Romain - 109-146 Estimating the effect of central bank independence on inflation using longitudinal targeted maximum likelihood estimation
by Baumann Philipp F. M. & Schomaker Michael & Rossi Enzo - 147-171 Designing experiments informed by observational studies
by Rosenman Evan T. R. & Owen Art B. - 172-189 Nonparametric inference for interventional effects with multiple mediators
by Benkeser David & Ran Jialu - 190-210 Novel bounds for causal effects based on sensitivity parameters on the risk difference scale
by Sjölander Arvid & Hössjer Ola - 211-228 Estimating causal effects with the neural autoregressive density estimator
by Garrido Sergio & Borysov Stanislav & Rich Jeppe & Pereira Francisco - 229-249 On the bias of adjusting for a non-differentially mismeasured discrete confounder
by Peña Jose M. & Balgi Sourabh & Sjölander Arvid & Gabriel Erin E. - 250-263 Learning linear non-Gaussian graphical models with multidirected edges
by Liu Yiheng & Robeva Elina & Wang Huanqing - 264-284 Conditional as-if analyses in randomized experiments
by Pashley Nicole E. & Basse Guillaume W. & Miratrix Luke W. - 285-301 Causal versions of maximum entropy and principle of insufficient reason
by Janzing Dominik - 302-344 Incremental intervention effects in studies with dropout and many timepoints#
by Kim Kwangho & Kennedy Edward H. & Naimi Ashley I. - 345-369 Optimal balancing of time-dependent confounders for marginal structural models
by Kallus Nathan & Santacatterina Michele
January 2020, Volume 8, Issue 1
- 1-21 Unifying Gaussian LWF and AMP Chain Graphs to Model Interference
by Peña Jose M. - 22-53 A Combinatorial Solution to Causal Compatibility
by Fraser Thomas C. - 22-53 A Combinatorial Solution to Causal Compatibility
by Fraser Thomas C. - 54-69 Post-randomization Biomarker Effect Modification Analysis in an HIV Vaccine Clinical Trial
by Gilbert Peter B. & Blette Bryan S. & Shepherd Bryan E. & Hudgens Michael G. - 54-69 Post-randomization Biomarker Effect Modification Analysis in an HIV Vaccine Clinical Trial
by Gilbert Peter B. & Blette Bryan S. & Hudgens Michael G. & Shepherd Bryan E. - 70-91 The Inflation Technique Completely Solves the Causal Compatibility Problem
by Navascués Miguel & Wolfe Elie - 70-91 The Inflation Technique Completely Solves the Causal Compatibility Problem
by Navascués Miguel & Wolfe Elie - 92-107 Averaging causal estimators in high dimensions
by Antonelli Joseph & Cefalu Matthew - 108-130 Estimating population average treatment effects from experiments with noncompliance
by Ottoboni Kellie N. & Poulos Jason V. - 131-149 A Two-Stage Joint Modeling Method for Causal Mediation Analysis in the Presence of Treatment Noncompliance
by Park Soojin & Kürüm Esra - 150-163 On the Monotonicity of a Nondifferentially Mismeasured Binary Confounder
by Peña Jose M. - 164-181 Beyond Manipulation: Administrative Sorting in Regression Discontinuity Designs
by Crespo Cristian - 182-208 Instruments with Heterogeneous Effects: Bias, Monotonicity, and Localness
by Huntington-Klein Nick - 209-228 When is a Match Sufficient? A Score-based Balance Metric for the Synthetic Control Method
by Parast Layla & Hunt Priscillia & Griffin Beth Ann & Powell David - 229-248 A note on a sensitivity analysis for unmeasured confounding, and the related E-value
by Sjölander Arvid - 249-271 Estimating Average Treatment Effects Utilizing Fractional Imputation when Confounders are Subject to Missingness
by Corder Nathan & Yang Shu - 272-285 Identification and Estimation of Intensive Margin Effects by Difference-in-Difference Methods
by Hersche Markus & Moor Elias - 286-299 Direct Effects under Differential Misclassification in Outcomes, Exposures, and Mediators
by Li Yige & VanderWeele Tyler J. - 300-314 Improved Doubly Robust Estimation in Marginal Mean Models for Dynamic Regimes
by Sun Hao & Ertefaie Ashkan & Lu Xin & Johnson Brent A.
January 2019, Volume 8, Issue 1
- 1-21 Unifying Gaussian LWF and AMP Chain Graphs to Model Interference
by Peña Jose M.
September 2019, Volume 7, Issue 2
- 1-8 Sufficient Causes: On Oxygen, Matches, and Fires
by Pearl Judea - 1-11 Technical Considerations in the Use of the E-Value
by VanderWeele Tyler J. & Ding Peng & Mathur Maya - 1-13 A Gaussian Process Framework for Overlap and Causal Effect Estimation with High-Dimensional Covariates
by Ghosh Debashis & Cruz Cortés Efrén - 1-36 Regression Adjustments for Estimating the Global Treatment Effect in Experiments with Interference
by Chin Alex - 1-51 The Inflation Technique for Causal Inference with Latent Variables
by Wolfe Elie & Spekkens Robert W. & Fritz Tobias - 1-51 The Inflation Technique for Causal Inference with Latent Variables
by Wolfe Elie & Spekkens Robert W. & Fritz Tobias
March 2019, Volume 7, Issue 1
- 1-4 A Falsifiability Characterization of Double Robustness Through Logical Operators
by Frangakis Constantine - 1-6 On the Interpretation of do(x)do(x)
by Pearl Judea - 1-8 Estimating Causal Effects of New Treatments Despite Self-Selection: The Case of Experimental Medical Treatments
by Hazlett Chad - 1-14 Learning Heterogeneity in Causal Inference Using Sufficient Dimension Reduction
by Luo Wei & Wu Wenbo & Zhu Yeying - 1-15 Estimating Mann–Whitney-Type Causal Effects for Right-Censored Survival Outcomes
by Zhang Zhiwei & Liu Chunling & Ma Shujie & Zhang Min - 1-16 The Entry of Randomized Assignment into the Social Sciences
by Jamison Julian C. - 1-19 New Traffic Conflict Measure Based on a Potential Outcome Model
by Yamada Kentaro & Kuroki Manabu - 1-24 Approximate Kernel-Based Conditional Independence Tests for Fast Non-Parametric Causal Discovery
by Strobl Eric V. & Zhang Kun & Visweswaran Shyam - 1-29 Randomization Tests that Condition on Non-Categorical Covariate Balance
by Branson Zach & Miratrix Luke W.
September 2018, Volume 6, Issue 2
- 1-7 Does Obesity Shorten Life? Or is it the Soda? On Non-manipulable Causes
by Pearl Judea - 1-12 Bayesian Inference of Causal Effects for an Ordinal Outcome in Randomized Trials
by Chiba Yasutaka - 1-17 Covariate Balancing Inverse Probability Weights for Time-Varying Continuous Interventions
by Huffman Curtis & van Gameren Edwin - 1-19 Propensity Score Weighting for Causal Inference with Clustered Data
by Yang Shu - 1-19 A Kernel-Based Metric for Balance Assessment
by Zhu Yeying & Savage Jennifer S. & Ghosh Debashis - 1-26 Synthetic Control Method: Inference, Sensitivity Analysis and Confidence Sets
by Firpo Sergio & Possebom Vitor - 1-35 Invariant Causal Prediction for Nonlinear Models
by Heinze-Deml Christina & Peters Jonas & Meinshausen Nicolai
March 2018, Volume 6, Issue 1
- 1-9 What is Gained from Past Learning
by Pearl Judea - 1-16 Variable Selection in Causal Inference using a Simultaneous Penalization Method
by Ertefaie Ashkan & Asgharian Masoud & Stephens David A. - 1-21 Determinantal Generalizations of Instrumental Variables
by Weihs Luca & Robinson Bill & Dufresne Emilie & Kenkel Jennifer & Kubjas Reginald McGee II Kaie & Reginald McGee II & Nguyen Nhan & Robeva Elina & Drton Mathias - 1-27 Detecting Confounding in Multivariate Linear Models via Spectral Analysis
by Janzing Dominik & Schölkopf Bernhard
September 2017, Volume 5, Issue 2
- 1-2 Erratum to: A Conditional Randomization Test for Covariate Imbalance
by Hennessy Jonathan & Dasgupta Tirthankar & Miratrix Luke & Pattanayak Cassandra & Pradipta Sarkar and - 1-8 Bridging Finite and Super Population Causal Inference
by Ding Peng & Li Xinran & Miratrix Luke W. - 1-10 Physical and Metaphysical Counterfactuals: Evaluating Disjunctive Actions
by Pearl Judea - 1-12 On Partial Identification of the Natural Indirect Effect
by Miles Caleb & Kanki Phyllis & Meloni Seema & Tchetgen Tchetgen Eric - 1-15 Counterfactual-Based Prevented and Preventable Proportions
by Yamada Kentaro & Kuroki Manabu - 1-16 A Simple Model Allowing Modification of the Effect of a Randomized Intervention by Post-Randomization Variables
by Faerber Jennifer A. & Joffe Marshall M. & Small Dylan S. & Zhang Rongmei & Brown Gregory K. & Ten Have Thomas R. - 1-18 Chasing Balance and Other Recommendations for Improving Nonparametric Propensity Score Models
by Griffin Beth Ann & McCaffrey Daniel F. & Almirall Daniel & Burgette Lane F. & Setodji Claude Messan - 1-24 Longitudinal Mediation Analysis with Time-varying Mediators and Exposures, with Application to Survival Outcomes
by Zheng Wenjing & van der Laan Mark - 1-26 Causal Inference via Algebraic Geometry: Feasibility Tests for Functional Causal Structures with Two Binary Observed Variables
by Lee Ciarán M. & Spekkens Robert W.
March 2017, Volume 5, Issue 1
- 1-10 Interventional Approach for Path-Specific Effects
by Lin Sheng-Hsuan & VanderWeele Tyler - 1-15 A Linear “Microscope” for Interventions and Counterfactuals
by Pearl Judea - 1-19 Entropy Balancing is Doubly Robust
by Zhao Qingyuan & Percival Daniel - 1-23 Design and Analysis of Experiments in Networks: Reducing Bias from Interference
by Eckles Dean & Karrer Brian & Ugander Johan - 1-35 Semi-Parametric Estimation and Inference for the Mean Outcome of the Single Time-Point Intervention in a Causally Connected Population
by Sofrygin Oleg & van der Laan Mark J. - 1-44 Identification of the Joint Effect of a Dynamic Treatment Intervention and a Stochastic Monitoring Intervention Under the No Direct Effect Assumption
by Neugebauer Romain & Schmittdiel Julie A. & Adams Alyce S. & Grant Richard W. & van der Laan Mark J.
September 2016, Volume 4, Issue 2
- 1-4 Data-Adaptive Causal Effects and Superefficiency
by Aronow Peter M. - 1-13 Lord’s Paradox Revisited – (Oh Lord! Kumbaya!)
by Pearl Judea - 1-17 Generalized Structural Mean Models for Evaluating Depression as a Post-treatment Effect Modifier of a Jobs Training Intervention
by Stephens Alisa & Keele Luke & Joffe Marshall - 1-19 A Causal Inference Approach to Network Meta-Analysis
by Schnitzer Mireille E & Steele Russell J & Bally Michèle & Shrier Ian - 1-22 The Mechanics of Omitted Variable Bias: Bias Amplification and Cancellation of Offsetting Biases
by Steiner Peter M. & Kim Yongnam
March 2016, Volume 4, Issue 1
- 1-30 Predicting Is Not Explaining: Targeted Learning of the Dative Alternation
by Chambaz Antoine & Desagulier Guillaume - 31-48 Markov Boundary Discovery with Ridge Regularized Linear Models
by Strobl Eric V. & Visweswaran Shyam - 49-59 Predicting the Direction of Causal Effect Based on an Instrumental Variable Analysis: A Cautionary Tale
by Burgess Stephen & Small Dylan S. - 61-80 A Conditional Randomization Test to Account for Covariate Imbalance in Randomized Experiments
by Hennessy Jonathan & Dasgupta Tirthankar & Miratrix Luke & Pattanayak Cassandra & Sarkar Pradipta - 81-86 The Sure-Thing Principle
by Pearl Judea
September 2015, Volume 3, Issue 2
- 139-155 Balancing Score Adjusted Targeted Minimum Loss-based Estimation
by Lendle Samuel David & Fireman Bruce & van der Laan Mark J. - 157-175 Surrogate Endpoint Evaluation: Principal Stratification Criteria and the Prentice Definition
by Gilbert Peter B. & Gabriel Erin E. & Huang Ying & Chan Ivan S.F. - 177-187 A Causal Perspective on OSIM2 Data Generation, with Implications for Simulation Study Design and Interpretation
by Gruber Susan - 189-205 Parameter Identifiability of Discrete Bayesian Networks with Hidden Variables
by Allman Elizabeth S. & Rhodes John A. & Stanghellini Elena & Valtorta Marco - 207-236 The Bayesian Causal Effect Estimation Algorithm
by Talbot Denis & Lefebvre Geneviève & Atherton Juli - 237-249 Propensity Score Analysis with Survey Weighted Data
by Ridgeway Greg & Kovalchik Stephanie Ann & Griffin Beth Ann & Kabeto Mohammed U. - 251-252 Reply to Professor Pearl’s Comment
by Ding Peng & Miratrix Luke W. - 253-258 M-bias, Butterfly Bias, and Butterfly Bias with Correlated Causes – A Comment on Ding and Miratrix (2015)
by Thoemmes Felix - 259-266 Generalizing Experimental Findings
by Pearl Judea
2015, Volume 3, Issue 1
- 1-24 Randomization Inference in the Regression Discontinuity Design: An Application to Party Advantages in the U.S. Senate
by Cattaneo Matias D. & Frandsen Brigham R. & Titiunik Rocío - 25-40 A Boosting Algorithm for Estimating Generalized Propensity Scores with Continuous Treatments
by Zhu Yeying & Coffman Donna L. & Ghosh Debashis - 41-57 To Adjust or Not to Adjust? Sensitivity Analysis of M-Bias and Butterfly-Bias
by Ding Peng & Miratrix Luke W. - 59-60 Comment on Ding and Miratrix: “To Adjust or Not to Adjust?”
by Pearl Judea - 61-95 Targeted Learning of the Mean Outcome under an Optimal Dynamic Treatment Rule
by van der Laan Mark J. & Luedtke Alexander R. - 97-108 On the Intersection Property of Conditional Independence and its Application to Causal Discovery
by Peters Jonas - 109-130 Assumption Trade-Offs When Choosing Identification Strategies for Pre-Post Treatment Effect Estimation: An Illustration of a Community-Based Intervention in Madagascar
by Weber Ann M. & van der Laan Mark J. & Petersen Maya L. - 131-137 Conditioning on Post-treatment Variables
by Pearl Judea
September 2014, Volume 2, Issue 2
- 115-129 The Deductive Approach to Causal Inference
by Pearl Judea - 131-146 Reducing Bias Amplification in the Presence of Unmeasured Confounding through Out-of-Sample Estimation Strategies for the Disease Risk Score
by Wyss Richard & Lunt Mark & Brookhart M. Alan & Glynn Robert J. & Stürmer Til - 147-185 Targeted Maximum Likelihood Estimation for Dynamic and Static Longitudinal Marginal Structural Working Models
by Petersen Maya & Schwab Joshua & Gruber Susan & Blaser Nello & Schomaker Michael & van der Laan Mark - 187-199 Testing for the Unconfoundedness Assumption Using an Instrumental Assumption
by de Luna Xavier & Johansson Per - 201-241 Causality, a Trialogue
by Chambaz Antoine & Drouet Isabelle & Thalabard Jean-Christophe - 243-248 Graphoids over Counterfactuals
by Pearl Judea - 249-249 Erratum to Is Scientific Knowledge Useful for Policy Analysis? A Peculiar Theorem Says: No [J Causal Inference DOI: 10.1515/jci-2014-0017]
by Pearl Judea
March 2014, Volume 2, Issue 1
- 1-12 Monotone Confounding, Monotone Treatment Selection and Monotone Treatment Response
by Jiang Zhichao & Chiba Yasutaka & VanderWeele Tyler J. - 13-74 Causal Inference for a Population of Causally Connected Units
by van der Laan Mark J. - 75-93 Confounding Equivalence in Causal Inference
by Pearl Judea & Paz Azaria - 95-108 Semiparametric Estimation of the Impacts of Longitudinal Interventions on Adolescent Obesity using Targeted Maximum-Likelihood: Accessible Estimation with the ltmle Package
by Decker Anna L. & Hubbard Alan & Crespi Catherine M. & Seto Edmund Y.W. & Wang May C.