Long-term Causal Inference via Modeling Sequential Latent Confounding

• URL: http://arxiv.org/abs/2502.18994v1
• Date: Wed, 26 Feb 2025 09:56:56 GMT
• Title: Long-term Causal Inference via Modeling Sequential Latent Confounding
• Authors: Weilin Chen, Ruichu Cai, Yuguang Yan, Zhifeng Hao, José Miguel Hernández-Lobato,
• Abstract summary: Ghassami et al. propose an approach based on the Conditional Additive Equi-Confounding Bias (CAECB) assumption.<n>We introduce a novel assumption that extends the CAECB assumption to accommodate temporal short-term outcomes.<n>Our proposed assumption states a functional relationship between sequential confounding biases across temporal short-term outcomes.
• Score: 49.64731441006396
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Long-term causal inference is an important but challenging problem across various scientific domains. To solve the latent confounding problem in long-term observational studies, existing methods leverage short-term experimental data. Ghassami et al. propose an approach based on the Conditional Additive Equi-Confounding Bias (CAECB) assumption, which asserts that the confounding bias in the short-term outcome is equal to that in the long-term outcome, so that the long-term confounding bias and the causal effects can be identified. While effective in certain cases, this assumption is limited to scenarios with a one-dimensional short-term outcome. In this paper, we introduce a novel assumption that extends the CAECB assumption to accommodate temporal short-term outcomes. Our proposed assumption states a functional relationship between sequential confounding biases across temporal short-term outcomes, under which we theoretically establish the identification of long-term causal effects. Based on the identification result, we develop an estimator and conduct a theoretical analysis of its asymptotic properties. Extensive experiments validate our theoretical results and demonstrate the effectiveness of the proposed method.

Related papers

• Nonparametric Heterogeneous Long-term Causal Effect Estimation via Data Combination [37.491679058742925]
  Long-term causal inference has drawn increasing attention in many scientific domains.<n>It is still understudied how to robustly and effectively estimate heterogeneous long-term causal effects.<n>We propose several two-stage style non-parametric estimators for heterogeneous long-term causal effect estimation.
  arXiv  Detail & Related papers  (2025-02-26T09:17:04Z)
• On the Identification of Temporally Causal Representation with Instantaneous Dependence [50.14432597910128]
  Temporally causal representation learning aims to identify the latent causal process from time series observations. Most methods require the assumption that the latent causal processes do not have instantaneous relations. We propose an textbfIDentification framework for instantanetextbfOus textbfLatent dynamics.
  arXiv  Detail & Related papers  (2024-05-24T08:08:05Z)
• Long-term Off-Policy Evaluation and Learning [21.047613223586794]
  Short- and long-term outcomes of an algorithm often differ, with damaging downstream effects. It takes months or even longer to observe the long-term outcomes of interest, making the algorithm selection process unacceptably slow. We propose a new framework called Long-term Off-Policy Evaluation (LOPE), which is based on reward function decomposition.
  arXiv  Detail & Related papers  (2024-04-24T06:59:59Z)
• TC-LIF: A Two-Compartment Spiking Neuron Model for Long-Term Sequential Modelling [54.97005925277638]
  The identification of sensory cues associated with potential opportunities and dangers is frequently complicated by unrelated events that separate useful cues by long delays. It remains a challenging task for state-of-the-art spiking neural networks (SNNs) to establish long-term temporal dependency between distant cues. We propose a novel biologically inspired Two-Compartment Leaky Integrate-and-Fire spiking neuron model, dubbed TC-LIF.
  arXiv  Detail & Related papers  (2023-08-25T08:54:41Z)
• 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)
• Estimating long-term causal effects from short-term experiments and long-term observational data with unobserved confounding [5.854757988966379]
  We study the identification and estimation of long-term treatment effects when both experimental and observational data are available. Our long-term causal effect estimator is obtained by combining regression residuals with short-term experimental outcomes.
  arXiv  Detail & Related papers  (2023-02-21T12:22:47Z)
• BaCaDI: Bayesian Causal Discovery with Unknown Interventions [118.93754590721173]
  BaCaDI operates in the continuous space of latent probabilistic representations of both causal structures and interventions. In experiments on synthetic causal discovery tasks and simulated gene-expression data, BaCaDI outperforms related methods in identifying causal structures and intervention targets.
  arXiv  Detail & Related papers  (2022-06-03T16:25:48Z)
• Long-term Causal Inference Under Persistent Confounding via Data Combination [38.026740610259225]
  We study the identification and estimation of long-term treatment effects when both experimental and observational data are available. Since the long-term outcome is observed only after a long delay, it is not measured in the experimental data, but only recorded in the observational data.
  arXiv  Detail & Related papers  (2022-02-15T07:44:20Z)
• ACRE: Abstract Causal REasoning Beyond Covariation [90.99059920286484]
  We introduce the Abstract Causal REasoning dataset for systematic evaluation of current vision systems in causal induction. Motivated by the stream of research on causal discovery in Blicket experiments, we query a visual reasoning system with the following four types of questions in either an independent scenario or an interventional scenario. We notice that pure neural models tend towards an associative strategy under their chance-level performance, whereas neuro-symbolic combinations struggle in backward-blocking reasoning.
  arXiv  Detail & Related papers  (2021-03-26T02:42:38Z)
• Long-Term Effect Estimation with Surrogate Representation [43.932546958874696]
  This work studies the problem of long-term effect where the outcome of primary interest, or primary outcome, takes months or even years to accumulate. We propose to build connections between long-term causal inference and sequential models in machine learning.
  arXiv  Detail & Related papers  (2020-08-19T03:16:18Z)

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.