Partial counterfactual identification and uplift modeling: theoretical results and real-world assessment

• URL: http://arxiv.org/abs/2211.07264v1
• Date: Mon, 14 Nov 2022 10:45:55 GMT
• Title: Partial counterfactual identification and uplift modeling: theoretical results and real-world assessment
• Authors: Th\'eo Verhelst, Denis Mercier, Jeevan Shrestha, Gianluca Bontempi
• Abstract summary: This paper discusses how it is possible to derive bounds on the probability of counterfactual statements based on uplift terms. We show that tightness of such bounds depends on the information of the feature set on the uplift term. We propose a point estimator based on the assumption of conditional independence between the counterfactual outcomes.
• Score: 0.4129225533930965
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Counterfactuals are central in causal human reasoning and the scientific discovery process. The uplift, also called conditional average treatment effect, measures the causal effect of some action, or treatment, on the outcome of an individual. This paper discusses how it is possible to derive bounds on the probability of counterfactual statements based on uplift terms. First, we derive some original bounds on the probability of counterfactuals and we show that tightness of such bounds depends on the information of the feature set on the uplift term. Then, we propose a point estimator based on the assumption of conditional independence between the counterfactual outcomes. The quality of the bounds and the point estimators are assessed on synthetic data and a large real-world customer data set provided by a telecom company, showing significant improvement over the state of the art.

Related papers

• Decoding Susceptibility: Modeling Misbelief to Misinformation Through a Computational Approach [61.04606493712002]
  Susceptibility to misinformation describes the degree of belief in unverifiable claims that is not observable. Existing susceptibility studies heavily rely on self-reported beliefs. We propose a computational approach to model users' latent susceptibility levels.
  arXiv  Detail & Related papers  (2023-11-16T07:22:56Z)
• Advancing Counterfactual Inference through Nonlinear Quantile Regression [77.28323341329461]
  We propose a framework for efficient and effective counterfactual inference implemented with neural networks. The proposed approach enhances the capacity to generalize estimated counterfactual outcomes to unseen data. Empirical results conducted on multiple datasets offer compelling support for our theoretical assertions.
  arXiv  Detail & Related papers  (2023-06-09T08:30:51Z)
• Data Association Aware POMDP Planning with Hypothesis Pruning Performance Guarantees [7.928094304325113]
  We introduce a pruning-based approach for planning with ambiguous data associations. Our key contribution is to derive bounds between the value function based on the complete set of hypotheses and the value function based on a pruned-subset of the hypotheses. We demonstrate how these bounds can both be used to certify any pruning in retrospect and propose a novel approach to determine which hypotheses to prune in order to ensure a predefined limit on the loss.
  arXiv  Detail & Related papers  (2023-03-03T18:35:01Z)
• 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)
• Bayesian Counterfactual Mean Embeddings and Off-Policy Evaluation [10.75801980090826]
  We present three novel Bayesian methods to estimate the expectation of the ultimate treatment effect. These methods differ on the source of uncertainty considered and allow for combining two sources of data. We generalize these ideas to the off-policy evaluation framework.
  arXiv  Detail & Related papers  (2022-11-02T23:39:36Z)
• 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)
• Federated Estimation of Causal Effects from Observational Data [19.657789891394504]
  We present a novel framework for causal inference with federated data sources. We assess and integrate local causal effects from different private data sources without centralizing them.
  arXiv  Detail & Related papers  (2021-05-31T08:06:00Z)
• Deconfounding Scores: Feature Representations for Causal Effect Estimation with Weak Overlap [140.98628848491146]
  We introduce deconfounding scores, which induce better overlap without biasing the target of estimation. We show that deconfounding scores satisfy a zero-covariance condition that is identifiable in observed data. In particular, we show that this technique could be an attractive alternative to standard regularizations.
  arXiv  Detail & Related papers  (2021-04-12T18:50:11Z)
• Causal Estimation with Functional Confounders [24.54466899641308]
  Causal inference relies on two fundamental assumptions: ignorability and positivity. We study causal inference when the true confounder value can be expressed as a function of the observed data. In this setting, ignorability is satisfied, however positivity is violated, and causal inference is impossible in general.
  arXiv  Detail & Related papers  (2021-02-17T02:16:21Z)
• A Class of Algorithms for General Instrumental Variable Models [29.558215059892206]
  Causal treatment effect estimation is a key problem that arises in a variety of real-world settings. We provide a method for causal effect bounding in continuous distributions.
  arXiv  Detail & Related papers  (2020-06-11T12:32:24Z)
• GenDICE: Generalized Offline Estimation of Stationary Values [108.17309783125398]
  We show that effective estimation can still be achieved in important applications. Our approach is based on estimating a ratio that corrects for the discrepancy between the stationary and empirical distributions. The resulting algorithm, GenDICE, is straightforward and effective.
  arXiv  Detail & Related papers  (2020-02-21T00:27:52Z)

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.