Robust and Heterogenous Odds Ratio: Estimating Price Sensitivity for Unbought Items

• URL: http://arxiv.org/abs/2106.11389v1
• Date: Mon, 21 Jun 2021 19:50:32 GMT
• Title: Robust and Heterogenous Odds Ratio: Estimating Price Sensitivity for Unbought Items
• Authors: Jean Pauphilet
• Abstract summary: Mining for heterogeneous responses to an intervention is a crucial step for data-driven operations. We investigate how to estimate price sensitivity from transaction-level data.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Problem definition: Mining for heterogeneous responses to an intervention is a crucial step for data-driven operations, for instance to personalize treatment or pricing. We investigate how to estimate price sensitivity from transaction-level data. In causal inference terms, we estimate heterogeneous treatment effects when (a) the response to treatment (here, whether a customer buys a product) is binary, and (b) treatment assignments are partially observed (here, full information is only available for purchased items). Methodology/Results: We propose a recursive partitioning procedure to estimate heterogeneous odds ratio, a widely used measure of treatment effect in medicine and social sciences. We integrate an adversarial imputation step to allow for robust inference even in presence of partially observed treatment assignments. We validate our methodology on synthetic data and apply it to three case studies from political science, medicine, and revenue management. Managerial Implications: Our robust heterogeneous odds ratio estimation method is a simple and intuitive tool to quantify heterogeneity in patients or customers and personalize interventions, while lifting a central limitation in many revenue management data.

Related papers

• Learning Representations of Instruments for Partial Identification of Treatment Effects [23.811079163083303]
  We leverage arbitrary (potentially high-dimensional) instruments to estimate bounds on the conditional average treatment effect (CATE) We propose a novel approach for partial identification through a mapping of instruments to a discrete representation space. We derive a two-step procedure that learns tight bounds using a tailored neural partitioning of the latent instrument space.
  arXiv  Detail & Related papers  (2024-10-11T16:48:32Z)
• Benchmarking Heterogeneous Treatment Effect Models through the Lens of Interpretability [82.29775890542967]
  Estimating personalized effects of treatments is a complex, yet pervasive problem. Recent developments in the machine learning literature on heterogeneous treatment effect estimation gave rise to many sophisticated, but opaque, tools. We use post-hoc feature importance methods to identify features that influence the model's predictions.
  arXiv  Detail & Related papers  (2022-06-16T17:59:05Z)
• Assessment of Treatment Effect Estimators for Heavy-Tailed Data [70.72363097550483]
  A central obstacle in the objective assessment of treatment effect (TE) estimators in randomized control trials (RCTs) is the lack of ground truth (or validation set) to test their performance. We provide a novel cross-validation-like methodology to address this challenge. We evaluate our methodology across 709 RCTs implemented in the Amazon supply chain.
  arXiv  Detail & Related papers  (2021-12-14T17:53:01Z)
• SurvITE: Learning Heterogeneous Treatment Effects from Time-to-Event Data [83.50281440043241]
  We study the problem of inferring heterogeneous treatment effects from time-to-event data. We propose a novel deep learning method for treatment-specific hazard estimation based on balancing representations.
  arXiv  Detail & Related papers  (2021-10-26T20:13:17Z)
• Estimating the Long-Term Effects of Novel Treatments [22.67249938461999]
  Policy makers typically face the problem of wanting to estimate the long-term effects of novel treatments. We propose a surrogate based approach where we assume that the long-term effect is channeled through a multitude of available short-term proxies.
  arXiv  Detail & Related papers  (2021-03-15T13:56:48Z)
• Split-Treatment Analysis to Rank Heterogeneous Causal Effects for Prospective Interventions [15.443178111068418]
  We propose a split-treatment analysis that ranks the individuals most likely to be positively affected by a prospective intervention. We show that the ranking of heterogeneous causal effect based on the proxy treatment is the same as the ranking based on the target treatment's effect.
  arXiv  Detail & Related papers  (2020-11-11T16:17:29Z)
• Enabling Counterfactual Survival Analysis with Balanced Representations [64.17342727357618]
  Survival data are frequently encountered across diverse medical applications, i.e., drug development, risk profiling, and clinical trials. We propose a theoretically grounded unified framework for counterfactual inference applicable to survival outcomes.
  arXiv  Detail & Related papers  (2020-06-14T01:15:00Z)
• Learning Decomposed Representation for Counterfactual Inference [53.36586760485262]
  The fundamental problem in treatment effect estimation from observational data is confounder identification and balancing. Most of the previous methods realized confounder balancing by treating all observed pre-treatment variables as confounders, ignoring further identifying confounders and non-confounders. We propose a synergistic learning framework to 1) identify confounders by learning representations of both confounders and non-confounders, 2) balance confounder with sample re-weighting technique, and simultaneously 3) estimate the treatment effect in observational studies via counterfactual inference.
  arXiv  Detail & Related papers  (2020-06-12T09:50:42Z)
• Counterfactual Propagation for Semi-Supervised Individual Treatment Effect Estimation [21.285425135761795]
  Individual treatment effect (ITE) represents the expected improvement in the outcome of taking a particular action to a particular target. In this study, we consider a semi-supervised ITE estimation problem that exploits more easily-available unlabeled instances. We propose counterfactual propagation, which is the first semi-supervised ITE estimation method.
  arXiv  Detail & Related papers  (2020-05-11T13:32:38Z)
• Generalization Bounds and Representation Learning for Estimation of Potential Outcomes and Causal Effects [61.03579766573421]
  We study estimation of individual-level causal effects, such as a single patient's response to alternative medication. We devise representation learning algorithms that minimize our bound, by regularizing the representation's induced treatment group distance. We extend these algorithms to simultaneously learn a weighted representation to further reduce treatment group distances.
  arXiv  Detail & Related papers  (2020-01-21T10:16:33Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.