Split-Treatment Analysis to Rank Heterogeneous Causal Effects for Prospective Interventions

• URL: http://arxiv.org/abs/2011.05877v1
• Date: Wed, 11 Nov 2020 16:17:29 GMT
• Title: Split-Treatment Analysis to Rank Heterogeneous Causal Effects for Prospective Interventions
• Authors: Yanbo Xu, Divyat Mahajan, Liz Manrao, Amit Sharma and Emre Kiciman
• Abstract summary: 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.
• Score: 15.443178111068418
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: For many kinds of interventions, such as a new advertisement, marketing intervention, or feature recommendation, it is important to target a specific subset of people for maximizing its benefits at minimum cost or potential harm. However, a key challenge is that no data is available about the effect of such a prospective intervention since it has not been deployed yet. In this work, we propose a split-treatment analysis that ranks the individuals most likely to be positively affected by a prospective intervention using past observational data. Unlike standard causal inference methods, the split-treatment method does not need any observations of the target treatments themselves. Instead it relies on observations of a proxy treatment that is caused by the target treatment. Under reasonable assumptions, 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. In the absence of any interventional data for cross-validation, Split-Treatment uses sensitivity analyses for unobserved confounding to select model parameters. We apply Split-Treatment to both a simulated data and a large-scale, real-world targeting task and validate our discovered rankings via a randomized experiment for the latter.

Related papers

• Nonparametric Identifiability of Causal Representations from Unknown Interventions [63.1354734978244]
  We study causal representation learning, the task of inferring latent causal variables and their causal relations from mixtures of the variables. Our goal is to identify both the ground truth latents and their causal graph up to a set of ambiguities which we show to be irresolvable from interventional data.
  arXiv  Detail & Related papers  (2023-06-01T10:51:58Z)
• Zero-shot causal learning [64.9368337542558]
  CaML is a causal meta-learning framework which formulates the personalized prediction of each intervention's effect as a task. We show that CaML is able to predict the personalized effects of novel interventions that do not exist at the time of training.
  arXiv  Detail & Related papers  (2023-01-28T20:14:11Z)
• Neighborhood Adaptive Estimators for Causal Inference under Network Interference [152.4519491244279]
  We consider the violation of the classical no-interference assumption, meaning that the treatment of one individuals might affect the outcomes of another. To make interference tractable, we consider a known network that describes how interference may travel. We study estimators for the average direct treatment effect on the treated in such a setting.
  arXiv  Detail & Related papers  (2022-12-07T14:53:47Z)
• Partial Identification of Dose Responses with Hidden Confounders [25.468473751289036]
  Inferring causal effects of continuous-valued treatments from observational data is a crucial task. We present novel methodology to bound both average and conditional average continuous-valued treatment-effect estimates. We apply our method to a real-world observational case study to demonstrate the value of identifying dose-dependent causal effects.
  arXiv  Detail & Related papers  (2022-04-24T07:02:21Z)
• Combining Experimental and Observational Data for Identification of Long-Term Causal Effects [13.32091725929965]
  We consider the task of estimating the causal effect of a treatment variable on a long-term outcome variable using data from an observational domain and an experimental domain. The observational data is assumed to be confounded and hence without further assumptions, this dataset alone cannot be used for causal inference either.
  arXiv  Detail & Related papers  (2022-01-26T04:21:14Z)
• BITES: Balanced Individual Treatment Effect for Survival data [0.0]
  Estimating the effects of interventions on patient outcome is one of the key aspects of personalized medicine. Time-to-event data is rarely used for treatment optimization. We suggest an approach named BITES, which combines a treatment-specific semi-parametric Cox loss with a treatment-balanced deep neural network.
  arXiv  Detail & Related papers  (2022-01-05T10:39:31Z)
• 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)
• Non parametric estimation of causal populations in a counterfactual scenario [6.247939901619901]
  We propose an innovative approach where the problem is reformulated as a missing data model. The aim is to estimate the hidden distribution of emphcausal populations, defined as a function of treatment and outcome.
  arXiv  Detail & Related papers  (2021-12-08T13:51:24Z)
• Efficient Causal Inference from Combined Observational and Interventional Data through Causal Reductions [68.6505592770171]
  Unobserved confounding is one of the main challenges when estimating causal effects. We propose a novel causal reduction method that replaces an arbitrary number of possibly high-dimensional latent confounders. We propose a learning algorithm to estimate the parameterized reduced model jointly from observational and interventional data.
  arXiv  Detail & Related papers  (2021-03-08T14:29:07Z)
• 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)
• Regret Minimization for Causal Inference on Large Treatment Space [21.957539112375496]
  We propose a network architecture and a regularizer that extracts a debiased representation from biased observational data. Our proposed loss minimizes a classification error of whether or not the action is relatively good for the individual target.
  arXiv  Detail & Related papers  (2020-06-10T02:19:48Z)

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.