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Statistics and causal inference.
 J. Am. Statist. Assoc.,
, 1986
"... Problems involving causal inference have dogged at the heels of statistics since its earliest days. Correlation does not imply causation, and yet causal conclusions drawn from a carefully designed experiment are often valid. What can a statistical model say about causation? This question is address ..."
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Cited by 736 (1 self)
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Problems involving causal inference have dogged at the heels of statistics since its earliest days. Correlation does not imply causation, and yet causal conclusions drawn from a carefully designed experiment are often valid. What can a statistical model say about causation? This question
in causal inference
, 2015
"... Background: The fundamental problem of causal inference is one of missing data, and specifically of missing potential outcomes: if potential outcomes were fully observed, then causal inference could be made trivially. Though often not discussed explicitly in the epidemiological literature, the conne ..."
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Background: The fundamental problem of causal inference is one of missing data, and specifically of missing potential outcomes: if potential outcomes were fully observed, then causal inference could be made trivially. Though often not discussed explicitly in the epidemiological literature
An Introduction to Causal Inference
 Causality in Crisis? University of Notre Dame
, 1997
"... developed a theory of statistical causal inference. In his presentation at the Notre Dame ..."
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Cited by 14 (0 self)
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developed a theory of statistical causal inference. In his presentation at the Notre Dame
Causal inference in multisensory perception
 PLoS ONE
, 2007
"... Perceptual events derive their significance to an animal from their meaning about the world, that is from the information they carry about their causes. The brain should thus be able to efficiently infer the causes underlying our sensory events. Here we use multisensory cue combination to study caus ..."
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Cited by 71 (9 self)
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causal inference in perception. We formulate an idealobserver model that infers whether two sensory cues originate from the same location and that also estimates their location(s). This model accurately predicts the nonlinear integration of cues by human subjects in two auditoryvisual localization
Causal inference in statistics: An Overview
, 2009
"... This review presents empirical researcherswith recent advances in causal inference, and stresses the paradigmatic shifts that must be undertaken in moving from traditional statistical analysis to causal analysis of multivariate data. Special emphasis is placed on the assumptions that underly all ca ..."
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Cited by 61 (12 self)
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This review presents empirical researcherswith recent advances in causal inference, and stresses the paradigmatic shifts that must be undertaken in moving from traditional statistical analysis to causal analysis of multivariate data. Special emphasis is placed on the assumptions that underly all
Theorybased causal inference
 In
, 2003
"... People routinely make sophisticated causal inferences unconsciously, effortlessly, and from very little data – often from just one or a few observations. We argue that these inferences can be explained as Bayesian computations over a hypothesis space of causal graphical models, shaped by strong top ..."
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Cited by 32 (4 self)
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People routinely make sophisticated causal inferences unconsciously, effortlessly, and from very little data – often from just one or a few observations. We argue that these inferences can be explained as Bayesian computations over a hypothesis space of causal graphical models, shaped by strong top
On Measurement Bias in Causal Inference
, 2010
"... This paper highlights several areas where graphical techniques can be harnessed to address the problem of measurement errors in causal inference. In particulars, the paper discusses the control of partially observable confounders in parametric and non parametric models and the computational problem ..."
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Cited by 11 (10 self)
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This paper highlights several areas where graphical techniques can be harnessed to address the problem of measurement errors in causal inference. In particulars, the paper discusses the control of partially observable confounders in parametric and non parametric models and the computational problem
Review Causal inference in perception
"... Until recently, the question of how the brain performs causal inference has been studied primarily in the context of cognitive reasoning. However, this problem is at least equally crucial in perceptual processing. At any given moment, the perceptual system receives multiple sensory signals within an ..."
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Cited by 7 (0 self)
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Until recently, the question of how the brain performs causal inference has been studied primarily in the context of cognitive reasoning. However, this problem is at least equally crucial in perceptual processing. At any given moment, the perceptual system receives multiple sensory signals within
On Measurement Bias in Causal Inference
, 2010
"... This paper addresses the problem of measurement errors in causal inference and highlights several algebraic and graphical methods for eliminating systematic bias induced by such errors. In particulars, the paper discusses the control of partially observable confounders in parametric and non parametr ..."
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This paper addresses the problem of measurement errors in causal inference and highlights several algebraic and graphical methods for eliminating systematic bias induced by such errors. In particulars, the paper discusses the control of partially observable confounders in parametric and non
Results 1  10
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