Results 101 to 110 of about 1,239,696 (252)

Bayesian Inference of Causal Effects for an Ordinal Outcome in Randomized Trials

Journal of Causal Inference, 2018
In randomized trials in which two treatment arms are compared with a binary outcome, the causal effect can be identified by assuming that the two treatment arms are exchangeable.
Chiba Yasutaka
doaj   +1 more source

The causal manipulation of chain event graphs [PDF]

, 2007
Discrete Bayesian Networks (BN’s) have been very successful as a framework both for inference and for expressing certain causal hypotheses. In this paper we present a class of graphical models called the chain event graph (CEG) models, that ...
Riccomagno, Eva, Smith, J. Q.
core  

Confounding Equivalence in Causal Inference

Journal of Causal Inference, 2014
The paper provides a simple test for deciding, from a given causal diagram, whether two sets of variables have the same bias-reducing potential under adjustment.
Pearl Judea, Paz Azaria
doaj   +1 more source

Causal reasoning and meta learning using kernel mean embeddings

, 2023
Kernel methods have been an essential instrument in machine learning over the years due to their ability to map data into high dimensional spaces efficiently.
Ton, Jean-Francois
core   +1 more source

A Conditional Randomization Test to Account for Covariate Imbalance in Randomized Experiments

Journal of Causal Inference, 2016
We consider the conditional randomization test as a way to account for covariate imbalance in randomized experiments. The test accounts for covariate imbalance by comparing the observed test statistic to the null distribution of the test statistic ...
Hennessy Jonathan, Dasgupta Tirthankar, Miratrix Luke, Pattanayak Cassandra, Sarkar Pradipta   +4 more
doaj   +1 more source

The causal manipulation and Bayesian estimation of chain event graphs [PDF]

, 2005
Discrete Bayesian Networks (BNs) have been very successful as a framework both for inference and for expressing certain causal hypotheses. In this paper we present a class of graphical models called the chain event graph (CEG) models, that generalises ...
Riccomagno, Eva, Smith, J. Q.
core  

Invariant Causal Prediction for Nonlinear Models

Journal of Causal Inference, 2018
An important problem in many domains is to predict how a system will respond to interventions. This task is inherently linked to estimating the system’s underlying causal structure.
Heinze-Deml Christina, Peters Jonas, Meinshausen Nicolai   +2 more
doaj   +1 more source

From urn models to box models: Making Neyman's (1923) insights accessible

Journal of Causal Inference
Neyman’s 1923 paper introduced the potential outcomes framework and the foundations of randomization-based inference. We discuss the influence of Neyman’s paper on four introductory to intermediate-level textbooks by Berkeley faculty members (Scheffé ...
Lin Winston, Dudoit Sandrine, Nolan Deborah, Speed Terence P.   +3 more
doaj   +1 more source

Approximate Kernel-Based Conditional Independence Tests for Fast Non-Parametric Causal Discovery

Journal of Causal Inference, 2019
Constraint-based causal discovery (CCD) algorithms require fast and accurate conditional independence (CI) testing. The Kernel Conditional Independence Test (KCIT) is currently one of the most popular CI tests in the non-parametric setting, but many ...
Strobl Eric V., Zhang Kun, Visweswaran Shyam   +2 more
doaj   +1 more source

Conditional generative adversarial networks for individualized causal mediation analysis

Journal of Causal Inference
Most classical methods popularly used in causal mediation analysis can only estimate the average causal effects and are difficult to apply to precision medicine.
Huan Cheng, Sun Rongqian, Song Xinyuan
doaj   +1 more source