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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… 
Wikipedia

Related topics

Related topics

11 relations
CausalityCorrelation does not imply causationMarginal structural modelPartial least squares regression

Broader (1)

Inductive reasoning

Papers overview

Semantic Scholar uses AI to extract papers important to this topic.
Highly Cited
2016
Highly Cited
2016
Causal Inference in Statistics: A Primer
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
Causal inference by using invariant prediction: identification and confidence intervals
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
Causal Inference without Balance Checking: Coarsened Exact Matching
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: Nonparametric Preprocessing for Parametric Causal Inference
MatchIt implements the suggestions of Ho, Imai, King, and Stuart (2007) for improving parametric statistical models by… 
Review
2010
Review
2010
Causal Inference
  • 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
Introduction to Causal Inference
  • 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
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… 
Highly Cited
2005
Highly Cited
2005
Causal Inference Using Potential Outcomes
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
Marginal Structural Models versus Structural nested Models as Tools for Causal inference
Robins (1993, 1994, 1997, 1998ab) has developed a set of causal or counterfactual models, the structural nested models (SNMs… 
Review
1999
Review
1999
Confounding and Collapsibility in Causal Inference
Consideration of confounding is fundamental to the design and analysis of studies of causal effects. Yet, apart from confounding… 
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