Estimating Treatment Effects from Irregular Time Series Observations with Hidden Confounders

• URL: http://arxiv.org/abs/2303.02320v1
• Date: Sat, 4 Mar 2023 04:55:34 GMT
• Title: Estimating Treatment Effects from Irregular Time Series Observations with Hidden Confounders
• Authors: Defu Cao, James Enouen, Yujing Wang, Xiangchen Song, Chuizheng Meng, Hao Niu, Yan Liu
• Abstract summary: Real-world time series can include large-scale, irregular, and intermittent time series observations. existence of hidden confounders can lead to biased treatment estimates. In continuous time settings with irregular samples, it is challenging to directly handle the dynamics of causality.
• Score: 15.41689729746877
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Causal analysis for time series data, in particular estimating individualized treatment effect (ITE), is a key task in many real-world applications, such as finance, retail, healthcare, etc. Real-world time series can include large-scale, irregular, and intermittent time series observations, raising significant challenges to existing work attempting to estimate treatment effects. Specifically, the existence of hidden confounders can lead to biased treatment estimates and complicate the causal inference process. In particular, anomaly hidden confounders which exceed the typical range can lead to high variance estimates. Moreover, in continuous time settings with irregular samples, it is challenging to directly handle the dynamics of causality. In this paper, we leverage recent advances in Lipschitz regularization and neural controlled differential equations (CDE) to develop an effective and scalable solution, namely LipCDE, to address the above challenges. LipCDE can directly model the dynamic causal relationships between historical data and outcomes with irregular samples by considering the boundary of hidden confounders given by Lipschitz-constrained neural networks. Furthermore, we conduct extensive experiments on both synthetic and real-world datasets to demonstrate the effectiveness and scalability of LipCDE.

Related papers

• Evidential time-to-event prediction model with well-calibrated uncertainty estimation [12.446406577462069]
  We introduce an evidential regression model designed especially for time-to-event prediction tasks. The most plausible event time is directly quantified by aggregated Gaussian random fuzzy numbers (GRFNs) Our model achieves both accurate and reliable performance, outperforming state-of-the-art methods.
  arXiv  Detail & Related papers  (2024-11-12T15:06:04Z)
• Causal Discovery from Time-Series Data with Short-Term Invariance-Based Convolutional Neural Networks [12.784885649573994]
  Causal discovery from time-series data aims to capture both intra-slice (contemporaneous) and inter-slice (time-lagged) causality. We propose a novel gradient-based causal discovery approach STIC, which focuses on textbfShort-textbfTerm textbfInvariance using textbfConvolutional neural networks.
  arXiv  Detail & Related papers  (2024-08-15T08:43:28Z)
• 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)
• Data Attribution for Diffusion Models: Timestep-induced Bias in Influence Estimation [53.27596811146316]
  Diffusion models operate over a sequence of timesteps instead of instantaneous input-output relationships in previous contexts. We present Diffusion-TracIn that incorporates this temporal dynamics and observe that samples' loss gradient norms are highly dependent on timestep. We introduce Diffusion-ReTrac as a re-normalized adaptation that enables the retrieval of training samples more targeted to the test sample of interest.
  arXiv  Detail & Related papers  (2024-01-17T07:58:18Z)
• Estimating Treatment Effects in Continuous Time with Hidden Confounders [8.292249583600809]
  Estimating treatment effects in the longitudinal setting in the presence of hidden confounders remains an extremely challenging problem. Recent advancements in neural differential equations to build a latent factor model using a controlled differential equation and Lipschitz constrained convolutional operation. Experiments on both synthetic and real-world datasets highlight the promise of continuous time methods for estimating treatment effects in the presence of hidden confounders.
  arXiv  Detail & Related papers  (2023-02-19T00:28:20Z)
• Continuous-Time Modeling of Counterfactual Outcomes Using Neural Controlled Differential Equations [84.42837346400151]
  Estimating counterfactual outcomes over time has the potential to unlock personalized healthcare. Existing causal inference approaches consider regular, discrete-time intervals between observations and treatment decisions. We propose a controllable simulation environment based on a model of tumor growth for a range of scenarios.
  arXiv  Detail & Related papers  (2022-06-16T17:15:15Z)
• 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)
• Deep Bayesian Estimation for Dynamic Treatment Regimes with a Long Follow-up Time [28.11470886127216]
  Causal effect estimation for dynamic treatment regimes (DTRs) contributes to sequential decision making. We combine outcome regression models with treatment models for high dimensional features using uncensored subjects that are small in sample size. Also, the developed deep Bayesian models can model uncertainty and output the prediction variance which is essential for the safety-aware applications, such as self-driving cars and medical treatment design.
  arXiv  Detail & Related papers  (2021-09-20T13:21:39Z)
• 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)
• DeepRite: Deep Recurrent Inverse TreatmEnt Weighting for Adjusting Time-varying Confounding in Modern Longitudinal Observational Data [68.29870617697532]
  We propose Deep Recurrent Inverse TreatmEnt weighting (DeepRite) for time-varying confounding in longitudinal data. DeepRite is shown to recover the ground truth from synthetic data, and estimate unbiased treatment effects from real data.
  arXiv  Detail & Related papers  (2020-10-28T15:05:08Z)

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.