Learning Disentangled Representations for Counterfactual Regression via Mutual Information Minimization

• URL: http://arxiv.org/abs/2206.01022v1
• Date: Thu, 2 Jun 2022 12:49:41 GMT
• Title: Learning Disentangled Representations for Counterfactual Regression via Mutual Information Minimization
• Authors: Mingyuan Cheng and Xinru Liao and Quan Liu and Bin Ma and Jian Xu and Bo Zheng
• Abstract summary: We propose Disentangled Representations for Counterfactual Regression via Mutual Information Minimization (MIM-DRCFR) We use a multi-task learning framework to share information when learning the latent factors and incorporates MI minimization learning criteria to ensure the independence of these factors. Experiments including public benchmarks and real-world industrial user growth datasets demonstrate that our method performs much better than state-of-the-art methods.
• Score: 25.864029391642422
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Learning individual-level treatment effect is a fundamental problem in causal inference and has received increasing attention in many areas, especially in the user growth area which concerns many internet companies. Recently, disentangled representation learning methods that decompose covariates into three latent factors, including instrumental, confounding and adjustment factors, have witnessed great success in treatment effect estimation. However, it remains an open problem how to learn the underlying disentangled factors precisely. Specifically, previous methods fail to obtain independent disentangled factors, which is a necessary condition for identifying treatment effect. In this paper, we propose Disentangled Representations for Counterfactual Regression via Mutual Information Minimization (MIM-DRCFR), which uses a multi-task learning framework to share information when learning the latent factors and incorporates MI minimization learning criteria to ensure the independence of these factors. Extensive experiments including public benchmarks and real-world industrial user growth datasets demonstrate that our method performs much better than state-of-the-art methods.

Related papers

• Multi-modal Causal Structure Learning and Root Cause Analysis [67.67578590390907]
  We propose Mulan, a unified multi-modal causal structure learning method for root cause localization. We leverage a log-tailored language model to facilitate log representation learning, converting log sequences into time-series data. We also introduce a novel key performance indicator-aware attention mechanism for assessing modality reliability and co-learning a final causal graph.
  arXiv  Detail & Related papers  (2024-02-04T05:50:38Z)
• Sample-Efficient Reinforcement Learning in the Presence of Exogenous Information [77.19830787312743]
  In real-world reinforcement learning applications the learner's observation space is ubiquitously high-dimensional with both relevant and irrelevant information about the task at hand. We introduce a new problem setting for reinforcement learning, the Exogenous Decision Process (ExoMDP), in which the state space admits an (unknown) factorization into a small controllable component and a large irrelevant component. We provide a new algorithm, ExoRL, which learns a near-optimal policy with sample complexity in the size of the endogenous component.
  arXiv  Detail & Related papers  (2022-06-09T05:19:32Z)
• Confounder Identification-free Causal Visual Feature Learning [84.28462256571822]
  We propose a novel Confounder Identification-free Causal Visual Feature Learning (CICF) method, which obviates the need for identifying confounders. CICF models the interventions among different samples based on front-door criterion, and then approximates the global-scope intervening effect upon the instance-level interventions. We uncover the relation between CICF and the popular meta-learning strategy MAML, and provide an interpretation of why MAML works from the theoretical perspective.
  arXiv  Detail & Related papers  (2021-11-26T10:57:47Z)
• 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)
• Learning Neural Causal Models with Active Interventions [83.44636110899742]
  We introduce an active intervention-targeting mechanism which enables a quick identification of the underlying causal structure of the data-generating process. Our method significantly reduces the required number of interactions compared with random intervention targeting. We demonstrate superior performance on multiple benchmarks from simulated to real-world data.
  arXiv  Detail & Related papers  (2021-09-06T13:10:37Z)
• Multi-Factors Aware Dual-Attentional Knowledge Tracing [0.0]
  We propose Multi-Factors Aware Dual-Attentional model (MF-DAKT) to model students' learning progress. To enrich questions representations, we use a pre-training method to incorporate two kinds of question information. We also apply a dual-attentional mechanism to differentiate contributions of factors and factor interactions to final prediction.
  arXiv  Detail & Related papers  (2021-08-10T15:03:28Z)
• Accurate and Robust Feature Importance Estimation under Distribution Shifts [49.58991359544005]
  PRoFILE is a novel feature importance estimation method. We show significant improvements over state-of-the-art approaches, both in terms of fidelity and robustness.
  arXiv  Detail & Related papers  (2020-09-30T05:29:01Z)
• Targeted VAE: Variational and Targeted Learning for Causal Inference [39.351088248776435]
  Undertaking causal inference with observational data is incredibly useful across a wide range of tasks. There are two significant challenges associated with undertaking causal inference using observational data. We address these two challenges by combining structured inference and targeted learning.
  arXiv  Detail & Related papers  (2020-09-28T16:55:24Z)

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.