SLOPE and Designing Robust Studies for Generalization

2025-10-01

AI Summary

In cross-population causal inference, unobserved differences between source and target populations frequently violate the conditional exchangeability assumption—an untestable condition critical for external validity. To address this, we propose SLOPE (Sensitivity to Local Policy Exogeneity), the first metric that integrates the Hampel-type derivative robustness framework with influence functions to quantify an estimator’s sensitivity to local violations of conditional exchangeability. SLOPE admits a closed-form analytical expression, enabling robustness comparisons across source/target population pairs or estimation strategies—even when the underlying assumptions are empirically unverifiable. We validate SLOPE through reanalyses of multi-country randomized experiments, demonstrating its ability to identify more robust generalization pathways. Our results enhance the reliability and reproducibility of causal inference designs in heterogeneous populations.

Technology Category

• Reasoning under Uncertainty: Causality
• Machine Learning: Causal Learning
• Natural Language Processing: Safety and Robustness

Application Category

• Security and Privacy: Large-scale security measurements
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods

Abstract

A popular task in generalization is to learn about a new, target population based on data from an existing, source population. This task relies on conditional exchangeability, which asserts that differences between the source and target populations are fully captured by observable characteristics of the two populations. Unfortunately, this assumption is often untenable in practice due to unobservable differences between the source and target populations. Worse, the assumption cannot be verified with data, warranting the need for robust data collection processes and study designs that are inherently less sensitive to violation of the assumption. In this paper, we propose SLOPE (Sensitivity of LOcal Perturbations from Exchangeability), a simple, intuitive, and novel measure that quantifies the sensitivity to local violation of conditional exchangeability. SLOPE combines ideas from sensitivity analysis in causal inference and derivative-based measure of robustness from Hampel (1974). Among other properties, SLOPE can help investigators to choose (a) a robust source or target population or (b) a robust estimand. Also, we show an analytic relationship between SLOPE and influence functions (IFs), which investigators can use to derive SLOPE given an IF. We conclude with a re-analysis of a multi-national randomized experiment and illustrate the role of SLOPE in informing robust study designs for generalization.

Problem

• Proposes SLOPE to measure sensitivity to exchangeability violations
• Addresses unobservable differences between source and target populations
• Helps design robust studies for generalization across populations

Innovation

• SLOPE measures sensitivity to exchangeability violations
• SLOPE combines sensitivity analysis with robustness measures
• SLOPE helps select robust populations and estimands