arXiv research

The paper develops fair machine learning models using causal path-specific effects.

New methods estimate causal effects through mediators, handling confounding without strict assumptions.

Study finds racial bias in pulse oximeter readings has minimal impact on ICU ventilation rates.

Proposes a method to learn fair classifiers without restrictive assumptions.

A recent trend of fair machine learning is to define fairness as causality-based notions which concern the causal connection between protected attributes and decisions. However, one common challenge of all causality-based fairness notions is identifiability, i.e., whether they can be uniquely measured from observationa…

We consider the problem of learning fair decision systems in complex scenarios in which a sensitive attribute might affect the decision along both fair and unfair pathways. We introduce a causal approach to disregard effects along unfair pathways that simplifies and generalizes previous literature. Our method corrects …

Predictive models learned from historical data are widely used to help companies and organizations make decisions. However, they may digitally unfairly treat unwanted groups, raising concerns about fairness and discrimination. In this paper, we study the fairness-aware ranking problem which aims to discover discriminat…

Proximal Mediation Analysis with Hidden Recanting Witnesses

Framework trains safe agents avoiding deceptive behavior.

New framework for fairness in continuous protected attributes.

Study introduces a new method to estimate causal mediation with multiple mediators.

A new method uses deep learning to evaluate causal theories without strict assumptions.

Regulating causal effects through averaged constraints fails to enforce conditional independence.

This work analyzes fairness-accuracy trade-offs using causal methods.

Recently there has been sustained interest in modifying prediction algorithms to satisfy fairness constraints. These constraints are typically complex nonlinear functionals of the observed data distribution. Focusing on the path-specific causal constraints proposed by Nabi and Shpitser (2018), we introduce new theoreti…

Cosine schedule is optimal for discrete diffusion models.

iCITRIS learns causal variables from interactive systems with instantaneous effects.

Identifies causal effects in LiNGAM models with latent variables.

BBCI uses meta prediction to estimate causal effects from datasets.

Bayesian model averaging improves causal effect estimation by averaging over multiple models.

Study clarifies variance of stratification estimators for causal effects.

Estimates causal effects in Gaussian Linear SCMs with finite data.

Despite the major advances taken in causal modeling, causality is still an unfamiliar topic for many statisticians. In this paper, it is demonstrated from the beginning to the end how causal effects can be estimated from observational data assuming that the causal structure is known. To make the problem more challengin…

SNAP efficiently identifies causal effects without needing full graph learning.

Paper identifies and estimates CAPCEs in continuous treatment settings.

We quantify causal bias in continuous treatment settings.

Proposes new method to handle hidden confounders in causal mediation analysis.

Algorithm finds causal effects from observational data using auxiliary variables.

New method estimates causal effects of multiple versions of treatment.

Estimates causal effects using neural autoregressive density estimators.

QPE identifies causal effects without assuming mechanisms or noise.

A new method selects covariates for causal effect estimation without strong assumptions.

Efficient algorithm for identifying causal effects in linear models.

This paper discusses the problem of causal query in observational data with hidden variables, with the aim of seeking the change of an outcome when "manipulating" a variable while given a set of plausible confounding variables which affect the manipulated variable and the outcome. Such an "experiment on data" to estima…

Optimizes causal effects on unknown graphs using Causal Entropy Optimization.

Study develops method for estimating causal effects in continuous variables.

This work addresses causal inference challenges in networked interference and proposes GNN-based estimators for individual treatment effects.

We consider the problem of learning causal models from observational data generated by linear non-Gaussian acyclic causal models with latent variables. Without considering the effect of latent variables, one usually infers wrong causal relationships among the observed variables. Under faithfulness assumption, we propos…

SD-SCMs generate counterfactual data for causal inference benchmarks.

New method infers causal effects without knowing control variables.

Transformer model handles causal inference with DAG integration.

Single proxy variable helps estimate causal effects from confounders.

An algorithm finds optimal covariates for blocking in randomized experiments.

SCBMs model causal effects using low-dimensional bottlenecks.

New bounds for causal effect identification in time series graphs with latent confounders.

New method estimates causal effects without knowing graph structure.

Estimates causal effect using proxies in multi-domain settings.

New framework for dynamic causal graph modeling and effect estimation.