Search Results for author:
Found 19 papers, 3 papers with code
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.
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.
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.
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.
Ancestral Instrument Method for Causal Inference without Complete Knowledge
Based on the theory, we develop an algorithm for unbiased causal effect estimation with a given ancestral IV and observational data.
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.
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.
Data-Driven Causal Effect Estimation Based on Graphical Causal Modelling: A Survey
In recent years, research has emerged to use search strategies based on graphical causal modelling to discover useful knowledge from data for causal effect estimation, with some mild assumptions, and has shown promise in tackling the practical challenge.
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.
Disentangled Representation for Causal Mediation Analysis
Causal mediation analysis is a method that is often used to reveal direct and indirect effects.
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.
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 Intervention for Measuring Confidence in Drug-Target Interaction Prediction
Identifying and discovering drug-target interactions(DTIs) are vital steps in drug discovery and development.
Learning Conditional Instrumental Variable Representation for Causal Effect Estimation
One of the fundamental challenges in causal inference is to estimate the causal effect of a treatment on its outcome of interest from observational data.
Conditional Instrumental Variable Regression with Representation Learning for Causal Inference
To address these challenging and practical problems of the standard IV method (linearity assumption and the strict condition), in this paper, we use a conditional IV (CIV) to relax the unconfounded instrument condition of standard IV and propose a non-linear CIV regression with Confounding Balancing Representation Learning, CBRL. CIV, for jointly eliminating the confounding bias from unobserved confounders and balancing the observed confounders, without the linearity assumption.
Causal Inference with Conditional Front-Door Adjustment and Identifiable Variational Autoencoder
In this paper, we relax some of the restrictions by proposing the concept of conditional front-door (CFD) adjustment and develop the theorem that guarantees the causal effect identifiability of CFD adjustment.
Disentangled Latent Representation Learning for Tackling the Confounding M-Bias Problem in Causal Inference
To address this problem with co-occurring M-bias and confounding bias, we propose a novel Disentangled Latent Representation learning framework for learning latent representations from proxy variables for unbiased Causal effect Estimation (DLRCE) from observational data.
Instrumental Variable Estimation for Causal Inference in Longitudinal Data with Time-Dependent Latent Confounders
Causal inference from longitudinal observational data is a challenging problem due to the difficulty in correctly identifying the time-dependent confounders, especially in the presence of latent time-dependent confounders.
Enhancing the Performance of Aspect-Based Sentiment Analysis Systems
Aspect-based sentiment analysis aims to predict sentiment polarity with fine granularity.
