# Evaluating Dynamic Conditional Quantile Treatment Effects with Applications in Ridesharing

arXiv: https://arxiv.org/abs/2305.10187 · HTML: https://mlsanity.com/paper/2305.10187/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 71 references that are in the map.
- First posted on arXiv 17 May 2023.

## Builds on (most cited first, 20 of 20)

- [Breaking the Curse of Horizon: Infinite-Horizon Off-Policy Estimation](https://mlsanity.com/paper/1810.12429/) (2018)
- [Efficiently Breaking the Curse of Horizon in Off-Policy Evaluation with Double Reinforcement Learning](https://mlsanity.com/paper/1909.05850/) (2019)
- [Batch Policy Learning in Average Reward Markov Decision Processes](https://mlsanity.com/paper/2007.11771/) (2020)
- [Statistical Inference of the Value Function for Reinforcement Learning in Infinite Horizon Settings](https://mlsanity.com/paper/2001.04515/) (2020)
- [Off-Policy Estimation of Long-Term Average Outcomes with Applications to Mobile Health](https://mlsanity.com/paper/1912.13088/) (2019)
- [Reinforcement Learning for Ridesharing: An Extended Survey](https://mlsanity.com/paper/2105.01099/) (2021)
- [Dynamic Causal Effects Evaluation in A/B Testing with a Reinforcement Learning Framework](https://mlsanity.com/paper/2002.01711/) (2020)
- [Bounded, efficient and multiply robust estimation of average treatment effects using instrumental variables](https://mlsanity.com/paper/1611.09925/) (2016)
- [Localized Debiased Machine Learning: Efficient Inference on Quantile Treatment Effects and Beyond](https://mlsanity.com/paper/1912.12945/) (2019)
- [A framework for Multi-A(rmed)/B(andit) testing with online FDR control](https://mlsanity.com/paper/1706.05378/) (2017)
- [Switchback Experiments under Geometric Mixing](https://mlsanity.com/paper/2209.00197/) (2022)
- [Graph-Based Equilibrium Metrics for Dynamic Supply-Demand Systems with Applications to Ride-sourcing Platforms](https://mlsanity.com/paper/2102.05805/) (2021)
- [Network Interference in Micro-Randomized Trials](https://mlsanity.com/paper/2202.05356/) (2022)
- [On Robustness of Individualized Decision Rules](https://mlsanity.com/paper/1903.04367/) (2019)
- [Cluster-Adaptive Network A/B Testing: From Randomization to Estimation](https://mlsanity.com/paper/2008.08648/) (2020)
- [Quantile Off-Policy Evaluation via Deep Conditional Generative Learning](https://mlsanity.com/paper/2212.14466/) (2022)
- [High-Dimensional Spatial Quantile Function-on-Scalar Regression](https://mlsanity.com/paper/2012.10579/) (2020)
- [A Multi-Agent Reinforcement Learning Framework for Off-Policy Evaluation in Two-sided Markets](https://mlsanity.com/paper/2202.10574/) (2022)
- [CONQ: CONtinuous Quantile Treatment Effects for Large-Scale Online Controlled Experiments](https://mlsanity.com/paper/2010.15326/) (2020)
- [Dynamic Car Dispatching and Pricing: Revenue and Fairness for Ridesharing Platforms](https://mlsanity.com/paper/2207.06318/) (2022)

## Built on by (most cited first, 7 of 7)

- [Unraveling the Interplay between Carryover Effects and Reward Autocorrelations in Switchback Experiments](https://mlsanity.com/paper/2403.17285/) (2024)
- [Combining Experimental and Historical Data for Policy Evaluation](https://mlsanity.com/paper/2406.00317/) (2024)
- [Balancing Interference and Correlation in Spatial Experimental Designs: A Causal Graph Cut Approach](https://mlsanity.com/paper/2505.20130/) (2025)
- [A Two-armed Bandit Framework for A/B Testing](https://mlsanity.com/paper/2507.18118/) (2025)
- [Designing Time Series Experiments in A/B Testing with Transformer Reinforcement Learning](https://mlsanity.com/paper/2602.01853/) (2026)
- [Robust Sequential Experimental Design for A/B Testing](https://mlsanity.com/paper/2605.12899/) (2026)
- [A Distribution Mapping Approach to Counterfactually Fair Reinforcement Learning](https://mlsanity.com/paper/2608.08743/) (2026)

## Abstract (arXiv)

Many modern tech companies, such as Google, Uber, and Didi, utilize online experiments (also known as A/B testing) to evaluate new policies against existing ones. While most studies concentrate on average treatment effects, situations with skewed and heavy-tailed outcome distributions may benefit from alternative criteria, such as quantiles. However, assessing dynamic quantile treatment effects (QTE) remains a challenge, particularly when dealing with data from ride-sourcing platforms that involve sequential decision-making across time and space. In this paper, we establish a formal framework to calculate QTE conditional on characteristics independent of the treatment. Under specific model assumptions, we demonstrate that the dynamic conditional QTE (CQTE) equals the sum of individual CQTEs across time, even though the conditional quantile of cumulative rewards may not necessarily equate to the sum of conditional quantiles of individual rewards. This crucial insight significantly streamlines the estimation and inference processes for our target causal 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 spatiotemporal dependent experiments and examine both conditional quantile direct and indirect effects. To showcase the practical utility of our method, we apply it to three real-world datasets from a ride-sourcing platform. Theoretical findings and comprehensive simulation studies further substantiate our proposal.