Using Causal Inference to Explore Government Policy Impact on Computer Usage
Journal:
arXiv
Published Date:
Mar 13, 2025
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.
