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Many modern tech companies, such as Google, Uber, and Didi, utilize onlineexperiments (also known as A/B testing) to evaluate new policies againstexisting ones. While most studies concentrate on average treatment effects,situations with skewed and heavy-tailed outcome distributions may benefit fromalternative criteria, such as quantiles. However, assessing dynamic quantiletreatment effects (QTE) remains a challenge, particularly when dealing withdata from ride-sourcing platforms that involve sequential decision-makingacross time and space. In this paper, we establish a formal framework tocalculate QTE conditional on characteristics independent of the treatment.Under specific model assumptions, we demonstrate that the dynamic conditionalQTE (CQTE) equals the sum of individual CQTEs across time, even though theconditional quantile of cumulative rewards may not necessarily equate to thesum of conditional quantiles of individual rewards. This crucial insightsignificantly streamlines the estimation and inference processes for our targetcausal estimand. We then introduce two varying coefficient decision process(VCDP) models and devise an innovative method to test the dynamic CQTE.Moreover, we expand our approach to accommodate data from spatiotemporaldependent experiments and examine both conditional quantile direct and indirecteffects. To showcase the practical utility of our method, we apply it to threereal-world datasets from a ride-sourcing platform. Theoretical findings andcomprehensive simulation studies further substantiate our proposal.

Evaluating Dynamic Conditional Quantile Treatment Effects with Applications in Ridesharing