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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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Related topics
Related topics
11 relations
Causality
Correlation does not imply causation
Marginal structural model
Partial least squares regression
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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
J. Pearl
,
M. Glymour
,
N. Jewell
2016
Corpus ID: 148322624
Many of the concepts and terminology surrounding modern causal inference can be quite intimidating to the novice. Judea Pearl…
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Highly Cited
2015
Highly Cited
2015
Causal inference by using invariant prediction: identification and confidence intervals
J. Peters
,
Peter Buhlmann
,
N. Meinshausen
2015
Corpus ID: 36882285
What is the difference between a prediction that is made with a causal model and that with a non‐causal model? Suppose that we…
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Highly Cited
2012
Highly Cited
2012
Causal Inference without Balance Checking: Coarsened Exact Matching
S. Iacus
,
Gary King
,
G. Porro
Political Analysis
2012
Corpus ID: 6323979
We discuss a method for improving causal inferences called “Coarsened Exact Matching” (CEM), and the new “Monotonic Imbalance…
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Highly Cited
2011
Highly Cited
2011
MatchIt: Nonparametric Preprocessing for Parametric Causal Inference
Daniel E. Ho
,
K. Imai
,
Gary King
,
E. Stuart
2011
Corpus ID: 715668
MatchIt implements the suggestions of Ho, Imai, King, and Stuart (2007) for improving parametric statistical models by…
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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…
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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…
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Highly Cited
2007
Highly Cited
2007
Matching as Nonparametric Preprocessing for Reducing Model Dependence in Parametric Causal Inference
Daniel E. Ho
,
K. Imai
,
Gary King
,
E. Stuart
Political Analysis
2007
Corpus ID: 38846
Although published works rarely include causal estimates from more than a few model specifications, authors usually choose the…
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Highly Cited
2005
Highly Cited
2005
Causal Inference Using Potential Outcomes
D. Rubin
2005
Corpus ID: 842793
Causal effects are defined as comparisons of potential outcomes under different treatments on a common set of units. Observed…
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Highly Cited
2000
Highly Cited
2000
Marginal Structural Models versus Structural nested Models as Tools for Causal inference
J. Robins
2000
Corpus ID: 17195289
Robins (1993, 1994, 1997, 1998ab) has developed a set of causal or counterfactual models, the structural nested models (SNMs…
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Review
1999
Review
1999
Confounding and Collapsibility in Causal Inference
S. Greenland
,
J. Robins
,
J. Pearl
1999
Corpus ID: 53499876
Consideration of confounding is fundamental to the design and analysis of studies of causal effects. Yet, apart from confounding…
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