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Found 22 papers, 5 papers with code
Disentangled Representation Learning for Causal Inference with Instruments
Latent confounders are a fundamental challenge for inferring causal effects from observational data.
Leaning Time-Varying Instruments for Identifying Causal Effects in Time-Series Data
Under the assumptions of the Markov property and availability of proxy variables, we theoretically establish the validity of these learned representations for addressing the biases from time-varying latent confounders, thus enabling accurate causal effect estimation.
Deconfounding Time Series Forecasting
Time series forecasting is a critical task in various domains, where accurate predictions can drive informed decision-making.
Linking Model Intervention to Causal Interpretation in Model Explanation
In this paper, we will study the conditions when an intuitive model intervention effect has a causal interpretation, i. e., when it indicates whether a feature is a direct cause of the outcome.
Fairness Evaluation with Item Response Theory
In this paper, we propose a novel Fair-IRT framework to evaluate a set of predictive models on a set of individuals, while simultaneously eliciting specific parameters, namely, the ability to make fair predictions (a feature of predictive models), as well as the discrimination and difficulty of individuals that affect the prediction results.
TSI: A Multi-View Representation Learning Approach for Time Series Forecasting
As the growing demand for long sequence time-series forecasting in real-world applications, such as electricity consumption planning, the significance of time series forecasting becomes increasingly crucial across various domains.
An Item Response Theory-based R Module for Algorithm Portfolio Analysis
The strengths and weaknesses of algorithms are visualised using the difficulty spectrum of the test instances.
Causal Effect Estimation using identifiable Variational AutoEncoder with Latent Confounders and Post-Treatment Variables
In this paper, we propose a novel method of joint Variational AutoEncoder (VAE) and identifiable Variational AutoEncoder (iVAE) for learning the representations of latent confounders and latent post-treatment variables from their proxy variables, termed CPTiVAE, to achieve unbiased causal effect estimation 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.
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.
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.
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.
Utilizing Semantic Textual Similarity for Clinical Survey Data Feature Selection
A relatively unexplored source of information in the feature selection process is the usage of textual names of features, which may be semantically indicative of which features are relevant to a target outcome.
Assisting Clinical Decisions for Scarcely Available Treatment via Disentangled Latent Representation
TVAE is specifically designed to address the modeling challenges like ECMO with strong treatment selection bias and scarce treatment cases.
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.
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.
Linking a predictive model to causal effect estimation
This paper first tackles the challenge of estimating the causal effect of any feature (as the treatment) on the outcome w. r. t.
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.
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.
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.
