313 citations · 1.6k across the 58 of their papers we have counts for
6 papers · 1 filter
Deep Learning Methods for Proximal Inference via Maximum Moment Restriction
Benjamin Kompa, David R. Bellamy, Thomas Kolokotrones +2
The No Unmeasured Confounding Assumption is widely used to identify causal effects in observational studies. Recent work on proximal inference has provided alternative identificati…
Identification In Missing Data Models Represented By Directed Acyclic Graphs
Rohit Bhattacharya, Razieh Nabi, Ilya Shpitser +1
Missing data is a pervasive problem in data analyses, resulting in datasets that contain censored realizations of a target distribution. Many approaches to inference on the target…
On nearly assumption-free tests of nominal confidence interval coverage for causal parameters estimated by machine learning
Lin Liu, Rajarshi Mukherjee, James M. Robins
For many causal effect parameters of interest, doubly robust machine learning (DRML) estimators are the state-of-the-art, incorporating the good prediction performance…
Double/Debiased Machine Learning for Treatment and Causal Parameters
Victor Chernozhukov, Denis Chetverikov, Mert Demirer +4
Most modern supervised statistical/machine learning (ML) methods are explicitly designed to solve prediction problems very well. Achieving this goal does not imply that these metho…
Influence Functions for Machine Learning: Nonparametric Estimators for Entropies, Divergences and Mutual Informations
Kirthevasan Kandasamy, Akshay Krishnamurthy, Barnabas Poczos +2
We propose and analyze estimators for statistical functionals of one or more distributions under nonparametric assumptions. Our estimators are based on the theory of influence func…
Parameter and Structure Learning in Nested Markov Models
Ilya Shpitser, Thomas S. Richardson, James M. Robins +1
The constraints arising from DAG models with latent variables can be naturally represented by means of acyclic directed mixed graphs (ADMGs). Such graphs contain directed and bidir…