DeepAI AI Chat
Log In Sign Up

Algorithmic recourse under imperfect causal knowledge: a probabilistic approach

06/11/2020
by   Amir-Hossein Karimi, et al.
19

Recent work has discussed the limitations of counterfactual explanations to recommend actions for algorithmic recourse, and argued for the need of taking causal relationships between features into consideration. Unfortunately, in practice, the true underlying structural causal model is generally unknown. In this work, we first show that it is impossible to guarantee recourse without access to the true structural equations. To address this limitation, we propose two probabilistic approaches to select optimal actions that achieve recourse with high probability given limited causal knowledge (e.g., only the causal graph). The first captures uncertainty over structural equations under additive Gaussian noise, and uses Bayesian model averaging to estimate the counterfactual distribution. The second removes any assumptions on the structural equations by instead computing the average effect of recourse actions on individuals similar to the person who seeks recourse, leading to a novel subpopulation-based interventional notion of recourse. We then derive a gradient-based procedure for selecting optimal recourse actions, and empirically show that the proposed approaches lead to more reliable recommendations under imperfect causal knowledge than non-probabilistic baselines.

READ FULL TEXT
06/22/2021

Algorithmic Recourse in Partially and Fully Confounded Settings Through Bounding Counterfactual Effects

Algorithmic recourse aims to provide actionable recommendations to indiv...
03/26/2023

Achieving Counterfactual Fairness with Imperfect Structural Causal Model

Counterfactual fairness alleviates the discrimination between the model ...
10/27/2022

Improvement-Focused Causal Recourse (ICR)

Algorithmic recourse recommendations, such as Karimi et al.'s (2021) cau...
11/03/2022

Decomposing Counterfactual Explanations for Consequential Decision Making

The goal of algorithmic recourse is to reverse unfavorable decisions (e....
06/30/2017

Probabilistic Active Learning of Functions in Structural Causal Models

We consider the problem of learning the functions computing children fro...
12/12/2012

Causes and Explanations in the Structural-Model Approach: Tractable Cases

In this paper, we continue our research on the algorithmic aspects of Ha...

Code Repositories

recourse

Code to reproduce our paper on probabilistic algorithmic recourse: https://arxiv.org/abs/2006.06831


view repo