Adversarial Debiasing for Unbiased Parameter Recovery

TL;DR

The paper tackles measurement-error bias that arises when machine-learned predictions serve as dependent variables in causal regressions. It introduces an adversarial debiasing framework that trains prediction models to minimize error while preventing information about the treatment variable from leaking into the residuals, thereby reducing $\\mathrm{Cov}(\\nu, X)$. It additionally provides a formal bias-detection test with a power-analysis workflow to quantify the required ground-truth labeling and demonstrates via simulations and a road-forest application that adversarial predictions yield unbiased parameter recovery where naive predictions do not. The approach offers practical, workflow-friendly gains for causal inference in remote sensing and related high-dimensional settings, along with important guidance on standard errors and inference under model uncertainty.

Abstract

Advances in machine learning and the increasing availability of high-dimensional data have led to the proliferation of social science research that uses the predictions of machine learning models as proxies for measures of human activity or environmental outcomes. However, prediction errors from machine learning models can lead to bias in the estimates of regression coefficients. In this paper, we show how this bias can arise, propose a test for detecting bias, and demonstrate the use of an adversarial machine learning algorithm in order to de-bias predictions. These methods are applicable to any setting where machine-learned predictions are the dependent variable in a regression. We conduct simulations and empirical exercises using ground truth and satellite data on forest cover in Africa. Using the predictions from a naive machine learning model leads to biased parameter estimates, while the predictions from the adversarial model recover the true coefficients.

Figures (9)

• Figure 1: The model architecture of the debiasing approach
• Figure 2: Colored areas show labeled pixels from bastin_extent_2017 --- green for forested and beige for non-forested. Inset shows an example of how percent forested labels were generated from high resolution satellite data.
• Figure 3: Estimates from the baseline model vs the adversarial models with 10,000 labeled observations. Each distribution represents the distribution of the coefficients from each model after 100 runs on bootstrapped training data.
• Figure 4: Estimates from the baseline model vs the adversarial models across sample sizes. Each light colored line is an individual training run where researchers label progressively more observations. The thick lines represent the mean and two standard deviations from the mean of the runs.
• Figure 5: Minimum detectable bias (MDB) across sample sizes at power of 0.8 and $\alpha = 0.05$. Each black line represents an estimate of MDB using a different random samples of labeled data, red line is the true MDB using standard errors estimated with the whole dataset.