arXiv subjects

Inessa Liskovich

Publications and source records attributed to Inessa Liskovich.

3 recordsLinked to original sources

arXiv2026

What is the Long-Term Value of Reliability?

We describe Chronos LTV, a system to measure the long-term impact of delays and other service defects on key business metrics. We use Markov decision processes to model customer interactions over time, and formalize our target estimand as the marginal policy effect with respect to moving the average delay rate. Given this setup, we show that we can identify long-term effects under a sequential unconfoundedness assumption where delays are as good as random given observed order characteristics; and can estimate these effects using a simple covariate-balancing algorithm.

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arXiv2021

Experimental Design in Two-Sided Platforms: An Analysis of Bias

We develop an analytical framework to study experimental design in two-sided marketplaces. Many of these experiments exhibit interference, where an intervention applied to one market participant influences the behavior of another participant. This interference leads to biased estimates of the treatment effect of the intervention. We develop a stochastic market model and associated mean field limit to capture dynamics in such experiments, and use our model to investigate how the performance of different designs and estimators is affected by marketplace interference effects. Platforms typically use two common experimental designs: demand-side ("customer") randomization (CR) and supply-side ("listing") randomization (LR), along with their associated estimators. We show that good experimental design depends on market balance: in highly demand-constrained markets, CR is unbiased, while LR is biased; conversely, in highly supply-constrained markets, LR is unbiased, while CR is biased. We also introduce and study a novel experimental design based on two-sided randomization (TSR) where both customers and listings are randomized to treatment and control. We show that appropriate choices of TSR designs can be unbiased in both extremes of market balance, while yielding relatively low bias in intermediate regimes of market balance.

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arXiv2020

Reducing Interference Bias in Online Marketplace Pricing Experiments

Online marketplace designers frequently run A/B tests to measure the impact of proposed product changes. However, given that marketplaces are inherently connected, total average treatment effect estimates obtained through Bernoulli randomized experiments are often biased due to violations of the stable unit treatment value assumption. This can be particularly problematic for experiments that impact sellers' strategic choices, affect buyers' preferences over items in their consideration set, or change buyers' consideration sets altogether. In this work, we measure and reduce bias due to interference in online marketplace experiments by using observational data to create clusters of similar listings, and then using those clusters to conduct cluster-randomized field experiments. We provide a lower bound on the magnitude of bias due to interference by conducting a meta-experiment that randomizes over two experiment designs: one Bernoulli randomized, one cluster randomized. In both meta-experiment arms, treatment sellers are subject to a different platform fee policy than control sellers, resulting in different prices for buyers. By conducting a joint analysis of the two meta-experiment arms, we find a large and statistically significant difference between the total average treatment effect estimates obtained with the two designs, and estimate that 32.60% of the Bernoulli-randomized treatment effect estimate is due to interference bias. We also find weak evidence that the magnitude and/or direction of interference bias depends on extent to which a marketplace is supply- or demand-constrained, and analyze a second meta-experiment to highlight the difficulty of detecting interference bias when treatment interventions require intention-to-treat analysis.

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