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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… Expand
Wikipedia

Related topics

Related topics

11 relations
CausalityCorrelation does not imply causationMarginal structural modelPartial least squares regression
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Broader (1)

Inductive reasoning

Papers overview

Semantic Scholar uses AI to extract papers important to this topic.
Review
2020
Review
2020
A Survey of Learning Causality with Data
This work considers the question of how convenient access to copious data impacts our ability to learn causal effects and… Expand
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Review
2019
Review
2019
Connecting the Dots: Identifying Network Structure via Graph Signal Processing
Network topology inference is a significant problem in network science. Most graph signal processing (GSP) efforts to date assume… Expand
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Review
2017
Review
2017
Causal Inference: A Missing Data Perspective
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
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… Expand
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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… Expand
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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 Inference Using Potential Outcomes
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
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… Expand
Highly Cited
1997
Highly Cited
1997
Causal Inference from Complex Longitudinal Data
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
Causal diagrams for empirical research
SUMMARY The primary aim of this paper is to show how graphical models can be used as a mathematical language for integrating… Expand
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