Regret Minimization for Causal Inference on Large Treatment Space

• URL: http://arxiv.org/abs/2006.05616v1
• Date: Wed, 10 Jun 2020 02:19:48 GMT
• Title: Regret Minimization for Causal Inference on Large Treatment Space
• Authors: Akira Tanimoto, Tomoya Sakai, Takashi Takenouchi, Hisashi Kashima
• Abstract summary: 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.
• Score: 21.957539112375496
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Predicting which action (treatment) will lead to a better outcome is a central task in decision support systems. To build a prediction model in real situations, learning from biased observational data is a critical issue due to the lack of randomized controlled trial (RCT) data. To handle such biased observational data, recent efforts in causal inference and counterfactual machine learning have focused on debiased estimation of the potential outcomes on a binary action space and the difference between them, namely, the individual treatment effect. When it comes to a large action space (e.g., selecting an appropriate combination of medicines for a patient), however, the regression accuracy of the potential outcomes is no longer sufficient in practical terms to achieve a good decision-making performance. This is because the mean accuracy on the large action space does not guarantee the nonexistence of a single potential outcome misestimation that might mislead the whole decision. Our proposed loss minimizes a classification error of whether or not the action is relatively good for the individual target among all feasible actions, which further improves the decision-making performance, as we prove. We also propose a network architecture and a regularizer that extracts a debiased representation not only from the individual feature but also from the biased action for better generalization in large action spaces. Extensive experiments on synthetic and semi-synthetic datasets demonstrate the superiority of our method for large combinatorial action spaces.

Related papers

• Reduced-Rank Multi-objective Policy Learning and Optimization [57.978477569678844]
  In practice, causal researchers do not have a single outcome in mind a priori. In government-assisted social benefit programs, policymakers collect many outcomes to understand the multidimensional nature of poverty. We present a data-driven dimensionality-reduction methodology for multiple outcomes in the context of optimal policy learning.
  arXiv  Detail & Related papers  (2024-04-29T08:16:30Z)
• Delving into Identify-Emphasize Paradigm for Combating Unknown Bias [52.76758938921129]
  We propose an effective bias-conflicting scoring method (ECS) to boost the identification accuracy. We also propose gradient alignment (GA) to balance the contributions of the mined bias-aligned and bias-conflicting samples. Experiments are conducted on multiple datasets in various settings, demonstrating that the proposed solution can mitigate the impact of unknown biases.
  arXiv  Detail & Related papers  (2023-02-22T14:50:24Z)
• Active Learning for Optimal Intervention Design in Causal Models [11.294389953686945]
  We develop a causal active learning strategy to identify interventions that are optimal, as measured by the discrepancy between the post-interventional mean of the distribution and a desired target mean. We apply our approach to both synthetic data and single-cell transcriptomic data from Perturb-CITE-seq experiments to identify optimal perturbations that induce a specific cell state transition.
  arXiv  Detail & Related papers  (2022-09-10T20:40:30Z)
• DESCN: Deep Entire Space Cross Networks for Individual Treatment Effect Estimation [7.060064266376701]
  Causal Inference has wide applications in various areas such as E-commerce and precision medicine. This paper proposes Deep Entire Space Cross Networks (DESCN) to model treatment effects from an end-to-end perspective.
  arXiv  Detail & Related papers  (2022-07-19T01:25:31Z)
• Causal-BALD: Deep Bayesian Active Learning of Outcomes to Infer Treatment-Effects from Observational Data [37.15330590319357]
  Existing approaches rely on fitting deep models on outcomes observed for treated and control populations. Deep Bayesian active learning provides a framework for efficient data acquisition by selecting points with high uncertainty. We introduce causal, Bayesian acquisition functions grounded in information theory that bias data acquisition towards regions with overlapping support.
  arXiv  Detail & Related papers  (2021-11-03T15:11:39Z)
• 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)
• 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)
• Double Robust Representation Learning for Counterfactual Prediction [68.78210173955001]
  We propose a novel scalable method to learn double-robust representations for counterfactual predictions. We make robust and efficient counterfactual predictions for both individual and average treatment effects. The algorithm shows competitive performance with the state-of-the-art on real world and synthetic data.
  arXiv  Detail & Related papers  (2020-10-15T16:39:26Z)
• 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)
• 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.