James M. Robins

Publication Activity (10 Years)

Years Active: 1995-2024
Publications (10 Years): 8

Top Topics

Directed Acyclic Graph

Models With Hidden Variables

Top Venues

J. Mach. Learn. Res.

Publications

Thomas S. Richardson, James M. Robins
Assumptions and Bounds in the Instrumental Variable Model. CoRR (2024)
Benjamin Kompa, David R. Bellamy, Thomas Kolokotrones, James M. Robins, Andrew Beam
Deep Learning Methods for Proximal Inference via Maximum Moment Restriction. NeurIPS (2022)
Razieh Nabi, Rohit Bhattacharya, Ilya Shpitser, James M. Robins
Causal and counterfactual views of missing data models. CoRR (2022)
Benjamin Kompa, David R. Bellamy, Thomas Kolokotrones, James M. Robins, Andrew L. Beam
Deep Learning Methods for Proximal Inference via Maximum Moment Restriction. CoRR (2022)
Lin Liu, Rajarshi Mukherjee, James M. Robins, Eric Tchetgen Tchetgen
Adaptive estimation of nonparametric functionals. J. Mach. Learn. Res. 22 (2021)
Rohit Bhattacharya, Razieh Nabi, Ilya Shpitser, James M. Robins
Identification In Missing Data Models Represented By Directed Acyclic Graphs. UAI (2019)
Lin Liu, Rajarshi Mukherjee, James M. Robins
On assumption-free tests and confidence intervals for causal effects estimated by machine learning. CoRR (2019)
Rohit Bhattacharya, Razieh Nabi, Ilya Shpitser, James M. Robins
Identification In Missing Data Models Represented By Directed Acyclic Graphs. CoRR (2019)
Kirthevasan Kandasamy, Akshay Krishnamurthy, Barnabás Póczos, Larry A. Wasserman, James M. Robins
Nonparametric von Mises Estimators for Entropies, Divergences and Mutual Informations. NIPS (2015)
Kirthevasan Kandasamy, Akshay Krishnamurthy, Barnabás Póczos, Larry A. Wasserman, James M. Robins
Influence Functions for Machine Learning: Nonparametric Estimators for Entropies, Divergences and Mutual Informations. CoRR (2014)
Ilya Shpitser, Robin J. Evans, Thomas S. Richardson, James M. Robins
Sparse Nested Markov models with Log-linear Parameters. CoRR (2013)
Judea Pearl, James M. Robins
Probabilistic Evaluation of Sequential Plans from Causal Models with Hidden Variables CoRR (2013)
Ilya Shpitser, Robin J. Evans, Thomas S. Richardson, James M. Robins
Sparse Nested Markov models with Log-linear Parameters. UAI (2013)
Ilya Shpitser, Thomas S. Richardson, James M. Robins
An Efficient Algorithm for Computing Interventional Distributions in Latent Variable Causal Models CoRR (2012)
Ilya Shpitser, Tyler J. VanderWeele, James M. Robins
On the Validity of Covariate Adjustment for Estimating Causal Effects CoRR (2012)
Thomas S. Richardson, James M. Robins, Ilya Shpitser
Nested Markov Properties for Acyclic Directed Mixed Graphs. UAI (2012)
Ilya Shpitser, Thomas S. Richardson, James M. Robins
An Efficient Algorithm for Computing Interventional Distributions in Latent Variable Causal Models. UAI (2011)
Jing Lei, James M. Robins, Larry A. Wasserman
Efficient Nonparametric Conformal Prediction Regions CoRR (2011)
Ilya Shpitser, Tyler J. VanderWeele, James M. Robins
On the Validity of Covariate Adjustment for Estimating Causal Effects. UAI (2010)
Ilya Shpitser, Thomas S. Richardson, James M. Robins
Testing Edges by Truncations. IJCAI (2009)
Tyler J. VanderWeele, James M. Robins
Properties of Monotonic Effects on Directed Acyclic Graphs. J. Mach. Learn. Res. 10 (2009)
James M. Robins
Association, Causation, And Marginal Structural Models. Synth. 121 (1-2) (1999)
James M. Robins, Larry A. Wasserman
Estimation of Effects of Sequential Treatments by Reparameterizing Directed Acyclic Graphs. UAI (1997)
Judea Pearl, James M. Robins
Probabilistic evaluation of sequential plans from causal models with hidden variables. UAI (1995)