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Found 7 papers, 6 papers with code
Inferring the Direction of a Causal Link and Estimating Its Effect via a Bayesian Mendelian Randomization Approach
In this paper, we propose a Bayesian approach called BayesMR that is a generalization of the Mendelian randomization technique in which we allow for pleiotropic effects and, crucially, for the possibility of reverse causation.
MASSIVE: Tractable and Robust Bayesian Learning of Many-Dimensional Instrumental Variable Models
Unfortunately, searching for proper instruments in a many-dimensional set of candidates is a daunting task due to the intractable model space and the fact that we cannot directly test which of these candidates are valid, so most existing search methods either rely on overly stringent modeling assumptions or fail to capture the inherent model uncertainty in the selection process.
Causal Shapley Values: Exploiting Causal Knowledge to Explain Individual Predictions of Complex Models
Shapley values underlie one of the most popular model-agnostic methods within explainable artificial intelligence.
Large-Scale Local Causal Inference of Gene Regulatory Relationships
Many of these computational methods are designed to infer individual regulatory relationships among genes from data on gene expression.
A Bayesian Approach for Inferring Local Causal Structure in Gene Regulatory Networks
Gene regulatory networks play a crucial role in controlling an organism's biological processes, which is why there is significant interest in developing computational methods that are able to extract their structure from high-throughput genetic data.
A Novel Bayesian Approach for Latent Variable Modeling from Mixed Data with Missing Values
We consider the problem of learning parameters of latent variable models from mixed (continuous and ordinal) data with missing values.
Robust Causal Estimation in the Large-Sample Limit without Strict Faithfulness
Causal effect estimation from observational data is an important and much studied research topic.
