Probabilistic evaluation of sequential plans from causal models with hidden variables. - Judea Pearl; James M. Robins (UAI, 1995)

Estimation of Effects of Sequential Treatments by Reparameterizing Directed Acyclic Graphs. - James M. Robins; Larry A. Wasserman (UAI, 1997)

Association, Causation, And Marginal Structural Models. - James M. Robins (Synth., 1999)

Estimation of the Bivariate Survival Function with Generalized Bivariate Right Censored Data Structures. - Sündüz Keles; Mark J. van der Laan; James M. Robins (Advances in Survival Analysis, 2003)

Testing Edges by Truncations. - Ilya Shpitser; Thomas S. Richardson 001; James M. Robins (IJCAI, 2009)

Properties of Monotonic Effects on Directed Acyclic Graphs. - Tyler J. VanderWeele; James M. Robins (J. Mach. Learn. Res., 2009)

On the Validity of Covariate Adjustment for Estimating Causal Effects. - Ilya Shpitser; Tyler J. VanderWeele; James M. Robins (UAI, 2010)

An Efficient Algorithm for Computing Interventional Distributions in Latent Variable Causal Models. - Ilya Shpitser; Thomas S. Richardson 001; James M. Robins (UAI, 2011)

Efficient Nonparametric Conformal Prediction Regions - Jing Lei 003; James M. Robins; Larry A. Wasserman (CoRR, 2011)

Nested Markov Properties for Acyclic Directed Mixed Graphs. - Thomas S. Richardson 001; James M. Robins; Ilya Shpitser (UAI, 2012)

Sparse Nested Markov models with Log-linear Parameters. - Ilya Shpitser; Robin J. Evans 002; Thomas S. Richardson 001; James M. Robins (UAI, 2013)

Influence Functions for Machine Learning: Nonparametric Estimators for Entropies, Divergences and Mutual Informations. - Kirthevasan Kandasamy; Akshay Krishnamurthy; Barnabás Póczos; Larry A. Wasserman; James M. Robins (CoRR, 2014)

Nonparametric von Mises Estimators for Entropies, Divergences and Mutual Informations. - Kirthevasan Kandasamy; Akshay Krishnamurthy; Barnabás Póczos; Larry A. Wasserman; James M. Robins (NIPS, 2015)

Identification In Missing Data Models Represented By Directed Acyclic Graphs. - Rohit Bhattacharya; Razieh Nabi; Ilya Shpitser; James M. Robins (UAI, 2019)

On assumption-free tests and confidence intervals for causal effects estimated by machine learning. - Lin Liu; Rajarshi Mukherjee; James M. Robins (CoRR, 2019)

Adaptive estimation of nonparametric functionals. - Lin Liu; Rajarshi Mukherjee; James M. Robins; Eric Tchetgen Tchetgen (J. Mach. Learn. Res., 2021)

An Interventionist Approach to Mediation Analysis. - James M. Robins; Thomas S. Richardson 001; Ilya Shpitser (Probabilistic and Causal Inference, 2022)

Multivariate Counterfactual Systems and Causal Graphical Models. - Ilya Shpitser; Thomas S. Richardson 001; James M. Robins (Probabilistic and Causal Inference, 2022)

Deep Learning Methods for Proximal Inference via Maximum Moment Restriction. - Benjamin Kompa; David R. Bellamy; Thomas Kolokotrones; James M. Robins; Andrew Beam (NeurIPS, 2022)

Causal and counterfactual views of missing data models. - Razieh Nabi; Rohit Bhattacharya; Ilya Shpitser; James M. Robins (CoRR, 2022)

Assumptions and Bounds in the Instrumental Variable Model. - Thomas S. Richardson 001; James M. Robins (CoRR, 2024)