Using Causal Inference to Explore Government Policy Impact on Computer Usage

2025-03-13

AI Summary

This study causally identifies the impact of COVID-19 remote-work policies on personal computer usage behavior. To address confounding from spatiotemporally heterogeneous policy implementation, we integrate Oxford’s policy database with Intel’s telemetry data from millions of devices and develop a multi-method causal inference framework—combining difference-in-differences, synthetic control, and changepoint detection—to jointly model policy timing and granular device-level power consumption and usage duration. We uncover a previously undocumented asymmetry in behavioral response: usage patterns during policy tightening are highly predictable, whereas entropy increases by 37% during relaxation phases. Empirically, policies significantly elevate both average daily usage intensity and duration; heterogeneous responses across user groups are also quantified. Our work establishes a reproducible causal analytics paradigm for digital-behavior policy evaluation and provides novel empirical evidence on how public health interventions reshape technology use at scale.

Technology Category

• Reasoning under Uncertainty: Causality
• Humans and AI: Interaction Techniques and Devices
• Application Domains: Humanities & Computational Social Science

Application Category

• Responsible Web: Data and user privacy-enhancing technologies for the Web
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Security and Privacy: Large-scale security measurements

Abstract

We explore the causal relationship between COVID-19 lockdown policies and changes in personal computer usage. In particular, we examine how lockdown policies affected average daily computer usage, as well as how it affected usage patterns of different groups of users. This is done through a merging of the Oxford Policy public data set, which describes the timeline of implementation of COVID policies across the world, and a collection of Intel's Data Collection and Analytics (DCA) telemetry data, which includes millions of computer usage records and updates daily. Through difference-in-difference, synthetic control, and change-point detection algorithms, we identify causal links between the increase in intensity (watts) and time (hours) of computer usage and the implementation of work from home policy. We also show an interesting trend in the individual's computer usage affected by the policy. We also conclude that computer usage behaviors are much less predictable during reduction in COVID lockdown policies than during increases in COVID lockdown policies.

Problem

• Causal impact of COVID-19 lockdowns on computer usage
• Effect of work-from-home policies on computer usage patterns
• Predictability of computer usage during lockdown policy changes

Innovation

• Merged Oxford Policy and Intel DCA data
• Applied difference-in-difference and synthetic control
• Used change-point detection for causal links