Search Results for author:
Found 12 papers, 2 papers with code
Ripple Knowledge Graph Convolutional Networks For Recommendation Systems
Using knowledge graphs to assist deep learning models in making recommendation decisions has recently been proven to effectively improve the model's interpretability and accuracy.
Causal Effect Estimation with Variational AutoEncoder and the Front Door Criterion
In practice, it is often difficult to identify the set of variables used for front-door adjustment from data.
Disentangled Representation for Causal Mediation Analysis
Causal mediation analysis is a method that is often used to reveal direct and indirect effects.
Causal Inference with Conditional Instruments using Deep Generative Models
The instrumental variable (IV) approach is a widely used way to estimate the causal effects of a treatment on an outcome of interest from observational data with latent confounders.
Data-Driven Causal Effect Estimation Based on Graphical Causal Modelling: A Survey
In recent years, research has emerged to use a search strategy based on graphical causal modelling to discover useful knowledge from data for causal effect estimation, with some mild assumptions, and has shown promose in tackling the practical challenge.
Disentangled Representation with Causal Constraints for Counterfactual Fairness
Much research has been devoted to the problem of learning fair representations; however, they do not explicitly the relationship between latent representations.
Discovering Ancestral Instrumental Variables for Causal Inference from Observational Data
Based on the theory, we propose a data-driven algorithm to discover a pair of IVs from data.
Ancestral instrument method for causal inference without a causal graph
Conditional IV has been proposed to relax the requirement of standard IV by conditioning on a set of observed variables (known as a conditioning set for a conditional IV).
Assessing Classifier Fairness with Collider Bias
The increasing application of machine learning techniques in everyday decision-making processes has brought concerns about the fairness of algorithmic decision-making.
Sufficient Dimension Reduction for Average Causal Effect Estimation
Having a large number of covariates can have a negative impact on the quality of causal effect estimation since confounding adjustment becomes unreliable when the number of covariates is large relative to the samples available.
Towards unique and unbiased causal effect estimation from data with hidden variables
Based on the theorems, two algorithms are proposed for finding the proper adjustment sets from data with hidden variables to obtain unbiased and unique causal effect estimation.
Causal query in observational data with hidden variables
In this paper, we develop a theorem for using local search to find a superset of the adjustment (or confounding) variables for causal effect estimation from observational data under a realistic pretreatment assumption.
