Results 111 to 120 of about 1,239,696 (252)

Causal inference for binary regression with observational data [PDF]

Special problems arise when trying to do causal inference for binary regression with observational data; we will examine some of these problems and critically examine several common and not-so-common solutions.
Austin Nichols
core  

Valid causal inference with unobserved confounding in high-dimensional settings

Various methods have recently been proposed to estimate causal effects with confidence intervals that are uniformly valid over a set of data-generating processes when high-dimensional nuisance models are estimated by post-model-selection or machine ...
Gorbach, Tetiana, de Luna, Xavier, Moosavi, Niloofar   +2 more
core   +1 more source

It was (not) me: Causal Inference of Agency in goal-directed actions

, 2011
Summary: The perception of one's own actions depends on both sensory information and predictions derived from internal forward models [1].
Martin A. Giese*, Tobias F. Beck, Axel Lindner, Dominik Endres, Dominik Endres*, Carlo Wilke, Martin Giese, Barbara Wirxel, Tobias Beck, Martin Giese*, Martin A. Giese   +10 more
core   +1 more source

Induction and causal inference

The general intention of the thesis is to provide an alternative to traditional inductivist and deductivist accounts of reasoning 'concerning matter of fact and existence' which not only more accurately characterises the inferential procedures which we ...
Lowe, E. Jonathan
core   +1 more source

Delay and knowledge mediation in human causal reasoning [PDF]

, 2002
Contemporary theories of causal induction have focussed largely on the question of how evidence in the form of covariations between causes and effects is used to compute measures of causal strength.
Buehner, Marc
core  

Individual differences in causal learning and decision making

, 2005
This is an accepted author manuscript of an article subsequently published by Elsevier. The final published version can be found here: http://dx.doi.org/10.1016/j.actpsy.2005.04.003In judgment and decision making tasks, people tend to neglect the overall
Osman, M, Shanks, David R
core   +1 more source

On identifying causal effects

, 2010
A variety of questions in causal inference can be represented as probability distributions over hypothetical worlds where idealized randomized experiments known as interventions have taken place.
Tian, Jin, Shpitser, Ilya
core  

EPR, Robustness and the Causal Markov Condition [PDF]

, 2007
It is still a matter of controversy whether the Principle of the Common Cause (PCC) can be used as a basis for sound causal inference. It is thus to be expected that its application to quantum mechanics should be a correspondingly controversial issue ...
San Pedro, Iñaki, Suárez, Mauricio
core  

Deep Learning for Causal Inference

CoRR, 2018
In this paper, we propose deep learning techniques for econometrics, specifically for causal inference and for estimating individual as well as average treatment effects. The contribution of this paper is twofold: 1. For generalized neighbor matching to estimate individual and average treatment effects, we analyze the use of autoencoders for ...
openaire   +2 more sources

Behavioural Causal Inference

Review of Economic Studies
Abstract When inferring causal effects from correlational data, a common practice by professional researchers but also lay people is to control for potential confounders. Inappropriate controls produce erroneous causal inferences. I model decision-makers (DMs) who use endogenous observational data to learn actions’ causal effect on ...
openaire   +2 more sources