Path-Specific Counterfactual Fairness Assessment

Description

A causal fairness evaluation technique that assesses algorithmic discrimination by examining specific causal pathways in a model's decision-making process. Unlike general counterfactual fairness, this approach enables practitioners to identify and intervene on particular causal paths that may introduce bias whilst preserving other legitimate pathways. The method uses causal graphs to distinguish between direct discrimination (through protected attributes) and indirect discrimination (through seemingly neutral factors that correlate with protected attributes), allowing for more nuanced fairness assessments in complex causal settings.

Example Use Cases

Fairness

Evaluating hiring algorithms by identifying which causal pathways from education and experience legitimately affect job performance versus those that introduce gender or racial bias, enabling targeted interventions that preserve merit-based selection whilst eliminating discriminatory pathways.

Transparency

Analysing loan approval models to provide transparent evidence of which factors legitimately influence creditworthiness versus those that create indirect discrimination, enabling clear explanations to regulators about causal mechanisms underlying fair lending decisions.

Reliability

Assessing medical diagnosis systems to ensure reliable performance by distinguishing between clinically relevant causal pathways (symptoms to diagnosis) and potentially biased pathways (demographics to diagnosis), maintaining diagnostic accuracy whilst preventing healthcare disparities.

Limitations

Requires identifying which causal pathways are 'allowable' and which are not—a subjective decision; analyzing specific paths adds complexity to the causal model and the fairness criterion.

Resources

Path-Specific Counterfactual Fairness via Dividend Correction

Research Paper•Daisuke Hatano, Satoshi Hara, and Hiromi Arai

Related Techniques

Name	Description	Assurance Goals
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Deep Ensembles	Deep ensembles combine predictions from multiple neural networks trained independently with different random initializations to capture epistemic uncertainty (model uncertainty). By training several models on the same data with different starting points, the ensemble reveals how much the model's predictions depend on training randomness. The disagreement between ensemble members naturally indicates prediction uncertainty - when models agree, confidence is high; when they disagree, uncertainty is revealed. This approach provides more reliable uncertainty estimates, better out-of-distribution detection, and improved calibration compared to single models.	Reliability Transparency Safety
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Conformal Prediction	Conformal prediction provides mathematically guaranteed uncertainty quantification by creating prediction sets that contain the true outcome with a specified probability (e.g., exactly 95% coverage). The technique works by measuring how 'strange' or 'nonconforming' new predictions are compared to calibration data - if a prediction seems unusual, it gets wider intervals. For example, in medical diagnosis, instead of saying 'likely cancer', it might say 'possible diagnoses: {cancer, benign tumour} with 95% confidence'. This distribution-free method works with any underlying model (neural networks, random forests, etc.) and requires no assumptions about data distribution, making it a robust framework for reliable uncertainty estimates in high-stakes applications.	Reliability Transparency Fairness
Sobol Indices	Sobol Indices quantify how much each input feature contributes to the total variance in a model's predictions through global sensitivity analysis. The technique calculates first-order indices (individual feature contributions) and total-order indices (including all interaction effects involving that feature). By systematically sampling the input space and decomposing output variance, Sobol Indices reveal which features drive model uncertainty and which interactions between features are most important for predictions.	Explainability Fairness