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Review

2020

Review

2020

This work considers the question of how convenient access to copious data impacts our ability to learn causal effects and… Expand

Review

2019

Review

2019

Network topology inference is a significant problem in network science. Most graph signal processing (GSP) efforts to date assume… Expand

Review

2017

Review

2017

Inferring causal effects of treatments is a central goal in many disciplines. The potential outcomes framework is a main… Expand

Highly Cited

2012

Highly Cited

2012

We discuss a method for improving causal inferences called ‘‘Coarsened Exact Matching’’ (CEM), and the new ‘‘Monotonic Imbalance… Expand

Highly Cited

2011

Highly Cited

2011

MatchIt implements the suggestions of Ho, Imai, King, and Stuart (2007) for improving parametric statistical models by… Expand

Highly Cited

2007

Highly Cited

2007

Matching as Nonparametric Preprocessing for Reducing Model Dependence in Parametric Causal Inference

Although published works rarely include causal estimates from more than a few model specifications, authors usually choose the… Expand

Highly Cited

2005

Highly Cited

2005

Causal effects are defined as comparisons of potential outcomes under different treatments on a common set of units. Observed… Expand

Highly Cited

2000

Highly Cited

2000

Robins (1993, 1994, 1997, 1998ab) has developed a set of causal or counterfactual models, the structural nested models (SNMs… Expand

Highly Cited

1997

Highly Cited

1997

The subject-specific data from a longitudinal study consist of a string of numbers. These numbers represent a series of empirical… Expand

Highly Cited

1995

Highly Cited

1995

SUMMARY The primary aim of this paper is to show how graphical models can be used as a mathematical language for integrating… Expand