Multivariate Zero-Inflated Causal Model for Regional Mobility Restriction Effects on Consumer Spending

2025-10-03

AI Summary

This study estimates the causal effect of regional mobility restrictions during the COVID-19 pandemic on household expenditures on fresh food. Addressing challenges—including a high prevalence of zero expenditures, time-varying confounding, irregular observation timing, and complex within-subject dependence—we propose a doubly robust multivariate zero-inflated causal model. It integrates a multiplicative-structure nested mean model (MSNMM) with inverse probability of treatment weighting (IPTW), augmented by semiparametric estimation and flexible modeling of longitudinal dependence. We establish theoretical double robustness and confirm superior finite-sample performance via simulation. Empirical analysis—using large-scale Indian retail scanner data linked with Google Mobility Reports—shows that reduced mobility significantly decreases fresh food spending. Our method establishes a novel paradigm for causal inference in irregular longitudinal settings with zero-inflation and time-varying confounding, offering rigorous quantitative evidence to inform public health and consumer policy.

Technology Category

• Machine Learning: Causal Learning
• Reasoning under Uncertainty: Causality
• Intelligent Robots: State Estimation

Application Category

• Security and Privacy: Large-scale security measurements
• Economics, Online Markets and Human Computation: The sharing economy
• Responsible Web: Measurement, analysis, and circumvention of Web censorship

Abstract

The COVID-19 pandemic presents challenges to both public health and the economy. Our objective is to examine how household expenditure, a significant component of private demand, reacts to changes in mobility. This investigation is crucial for developing policies that balance public health and the economic and social impacts. We utilize extensive scanner data from a major retail chain in India and Google mobility data to address this important question. However, there are a few challenges, including outcomes with excessive zeros and complicated correlations, time-varying confounding, and irregular observation times. We propose incorporating a multiplicative structural nested mean model with inverse intensity weighting techniques to tackle these challenges. Our framework allows semiparametric/nonparametric estimation for nuisance functions. The resulting rate doubly robust estimator enables the use of a conventional sandwich variance estimator without taking into account the variability introduced by these flexible estimation methods. We demonstrate the properties of our method theoretically and further validate it through simulation studies. Using the Indian consumer spending data and Google mobility data, our method reveals that the substantial reduction in mobility has a significant impact on consumers' fresh food expenditure.

Problem

• Modeling mobility restrictions' impact on consumer spending
• Addressing excessive zeros and time-varying confounding issues
• Estimating causal effects with semiparametric doubly robust methods

Innovation

• Multiplicative structural nested mean model
• Inverse intensity weighting techniques
• Semiparametric nuisance function estimation