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Causal inference

Causal inference is the process of drawing a conclusion about a causal connection based on the conditions of the occurrence of an effect. The main… 
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Papers overview

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Highly Cited
2016
Highly Cited
2016
Many of the concepts and terminology surrounding modern causal inference can be quite intimidating to the novice. Judea Pearl… 
Highly Cited
2015
Highly Cited
2015
What is the difference between a prediction that is made with a causal model and that with a non‐causal model? Suppose that we… 
Highly Cited
2012
Highly Cited
2012
We discuss a method for improving causal inferences called “Coarsened Exact Matching” (CEM), and the new “Monotonic Imbalance… 
Highly Cited
2011
Highly Cited
2011
MatchIt implements the suggestions of Ho, Imai, King, and Stuart (2007) for improving parametric statistical models by… 
Review
2010
Review
2010
  • J. Pearl
  • NIPS Causality: Objectives and Assessment
  • 2010
  • Corpus ID: 3057769
This paper reviews a theory of causal inference based on the Structural Causal Model (SCM) described in (Pearl, 2000a). The… 
Highly Cited
2010
Highly Cited
2010
  • P. Spirtes
  • J. Mach. Learn. Res.
  • 2010
  • Corpus ID: 1037969
The goal of many sciences is to understand the mechanisms by which variables came to take on the values they have (that is, to… 
Highly Cited
2007
Highly Cited
2007
Although published works rarely include causal estimates from more than a few model specifications, authors usually choose the… 
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… 
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… 
Review
1999
Review
1999
Consideration of confounding is fundamental to the design and analysis of studies of causal effects. Yet, apart from confounding…