# Using Causal Inference to Explore Government Policy Impact on Computer Usage

arXiv: https://arxiv.org/abs/2503.09957 · HTML: https://mlsanity.com/paper/2503.09957/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Mar 2025.

## Built on by (most cited first, 1 of 1)

- [BenchPress: A Human-in-the-Loop Annotation System for Rapid Text-to-SQL Benchmark Curation](https://mlsanity.com/paper/2510.13853/) (2025)

## Abstract (arXiv)

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.