  Many internet platforms that collect behavioral big data use it to predict
user behavior for internal purposes and for their business customers (e.g.,
advertisers, insurers, security forces, governments, political consulting
firms) who utilize the predictions for personalization, targeting, and other
decision-making. Improving predictive accuracy is therefore extremely valuable.
Data science researchers design algorithms, models, and approaches to improve
prediction. Prediction is also improved with larger and richer data. Beyond
improving algorithms and data, platforms can stealthily achieve better
prediction accuracy by pushing users' behaviors towards their predicted values,
using behavior modification techniques, thereby demonstrating more certain
predictions. Such apparent "improved" prediction can result from employing
reinforcement learning algorithms that combine prediction and behavior
modification. This strategy is absent from the machine learning and statistics
literature. Investigating its properties requires integrating causal with
predictive notation. To this end, we incorporate Pearl's causal do(.) operator
into the predictive vocabulary. We then decompose the expected prediction error
given behavior modification, and identify the components impacting predictive
power. Our derivation elucidates implications of such behavior modification to
data scientists, platforms, their customers, and the humans whose behavior is
manipulated. Behavior modification can make users' behavior more predictable
and even more homogeneous; yet this apparent predictability might not
generalize when business customers use predictions in practice. Outcomes pushed
towards their predictions can be at odds with customers' intentions, and
harmful to manipulated users.
