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Found 33 papers, 4 papers with code
Disentangled Representation Learning for Causal Inference with Instruments
Latent confounders are a fundamental challenge for inferring causal effects from observational data.
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.
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.
Is it Still Fair? A Comparative Evaluation of Fairness Algorithms through the Lens of Covariate Drift
Yet still, very little attention has been paid to the problem of naturally occurring changes in data patterns (\textit{aka} data distributional drift), and its impact on fairness algorithms and metrics.
A Deconfounding Approach to Climate Model Bias Correction
This paper proposes a novel bias correction approach to utilize both GCM and observational data to learn a factor model that captures multi-cause latent confounders.
A transformer boosted UNet for smoke segmentation in complex backgrounds in multispectral LandSat imagery
To show the advantages of the proposed model, the paper presents extensive results for various possible model architectures improving UNet and draws interesting conclusions including that adding more modules to a model does not always lead to a better performance.
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.
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.
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.
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.
Explanatory causal effects for model agnostic explanations
This paper studies the problem of estimating the contributions of features to the prediction of a specific instance by a machine learning model and the overall contribution of a feature to the model.
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 Complete Knowledge
Based on the theory, we develop an algorithm for unbiased causal effect estimation with a given ancestral IV and observational data.
Dependency-based Anomaly Detection: a General Framework and Comprehensive Evaluation
Anomaly detection is crucial for understanding unusual behaviors in data, as anomalies offer valuable insights.
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.
A general framework for causal classification
We also propose a general framework for causal classification, by using off-the-shelf supervised methods for flexible implementations.
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.
Linking Graph Entities with Multiplicity and Provenance
The graph model is versatile, thus, it is capable of handling multiple values for an attribute or a relationship, as well as the provenance descriptions of the values.
Identify treatment effect patterns for personalised decisions
The existing interpretable modelling methods take a top-down approach to search for subgroups with heterogeneous treatment effects and they may miss the most specific and relevant context for an individual.
An exploration of algorithmic discrimination in data and classification
The paper uses real world data sets to demonstrate the existence of discrimination and the independence between the discrimination of data sets and the discrimination of classification models.
FairMod - Making Predictive Models Discrimination Aware
It models the post-processing of predictions problem as a nonlinear optimization problem to find best adjustments to the predictions so that the discrimination constraints of all protected variables are all met at the same time.
Building Diversified Multiple Trees for Classification in High Dimensional Noisy Biomedical Data
It is common that a trained classification model is applied to the operating data that is deviated from the training data because of noise.
From Observational Studies to Causal Rule Mining
Specifically, association rule mining can be used to deal with the high-dimensionality problem while observational studies can be utilised to eliminate non-causal associations.
Causal Decision Trees
Classification methods are fast and they could be practical substitutes for finding causal signals in data.
