Population Size Estimation with Many Lists and Heterogeneity: A Conditional Log-Linear Model Among the Unobserved

TL;DR

This paper introduces a flexible, interpretable framework for estimating population size with multiple capture-recapture lists, accommodating heterogeneity and dependence among lists, and employing machine learning for nonparametric estimation.

Contribution

It develops a novel identification strategy that generalizes existing models, along with doubly-robust estimators and sensitivity analysis, applicable to complex heterogeneous capture probabilities.

Findings

Method performs well on synthetic data.

Application to Peruvian conflict data estimates casualties.

Addresses critiques by relaxing assumptions and incorporating covariates.

Abstract

We contribute a general and flexible framework to estimate the size of a closed population in the presence of $K$ capture-recapture lists and heterogeneous capture probabilities. Our novel identifying strategy leverages the fact that it is sufficient for identification that a subset of the $K$ lists are not arbitrarily dependent \textit{within the subset of the population unobserved by the remaining lists}, conditional on covariates. This identification approach is interpretable and actionable, interpolating between the two predominant approaches in the literature as special cases: (conditional) independence across lists and log-linear models with no highest-order interaction. We derive nonparametric doubly-robust estimators for the resulting identification expression that are nearly optimal and approximately normal for any finite sample size, even when the heterogeneous capture probabilities are estimated nonparametrically using machine learning methods. Additionally, we devise a sensitivity analysis to show how deviations from the identification assumptions affect the resulting population size estimates, allowing for the integration of domain-specific knowledge into the identification and estimation processes more transparently. We empirically demonstrate the advantages of our method using both synthetic data and real data from the Peruvian internal armed conflict to estimate the number of casualties. The proposed methodology addresses recent critiques of capture-recapture models by allowing for a weaker and more interpretable identifying assumption and accommodating complex heterogeneous capture probabilities depending on high-dimensional or continuous covariates.