Stochastic Intervention for Causal Inference via Reinforcement Learning
- URL: http://arxiv.org/abs/2105.13514v1
- Date: Fri, 28 May 2021 00:11:22 GMT
- Title: Stochastic Intervention for Causal Inference via Reinforcement Learning
- Authors: Tri Dung Duong, Qian Li, Guandong Xu
- Abstract summary: Central to causal inference is the treatment effect estimation of intervention strategies.
Existing methods are mostly restricted to the deterministic treatment and compare outcomes under different treatments.
We propose a new effective framework to estimate the treatment effect on intervention.
- Score: 7.015556609676951
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Causal inference methods are widely applied in various decision-making
domains such as precision medicine, optimal policy and economics. Central to
causal inference is the treatment effect estimation of intervention strategies,
such as changes in drug dosing and increases in financial aid. Existing methods
are mostly restricted to the deterministic treatment and compare outcomes under
different treatments. However, they are unable to address the substantial
recent interest of treatment effect estimation under stochastic treatment,
e.g., "how all units health status change if they adopt 50\% dose reduction".
In other words, they lack the capability of providing fine-grained treatment
effect estimation to support sound decision-making. In our study, we advance
the causal inference research by proposing a new effective framework to
estimate the treatment effect on stochastic intervention. Particularly, we
develop a stochastic intervention effect estimator (SIE) based on nonparametric
influence function, with the theoretical guarantees of robustness and fast
convergence rates. Additionally, we construct a customised reinforcement
learning algorithm based on the random search solver which can effectively find
the optimal policy to produce the greatest expected outcomes for the
decision-making process. Finally, we conduct an empirical study to justify that
our framework can achieve significant performance in comparison with
state-of-the-art baselines.
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) - Targeted Machine Learning for Average Causal Effect Estimation Using the
Front-Door Functional [3.0232957374216953]
evaluating the average causal effect (ACE) of a treatment on an outcome often involves overcoming the challenges posed by confounding factors in observational studies.
Here, we introduce novel estimation strategies for the front-door criterion based on the targeted minimum loss-based estimation theory.
We demonstrate the applicability of these estimators to analyze the effect of early stage academic performance on future yearly income.
arXiv Detail & Related papers (2023-12-15T22:04:53Z) - The Blessings of Multiple Treatments and Outcomes in Treatment Effect
Estimation [53.81860494566915]
Existing studies leveraged proxy variables or multiple treatments to adjust for confounding bias.
In many real-world scenarios, there is greater interest in studying the effects on multiple outcomes.
We show that parallel studies of multiple outcomes involved in this setting can assist each other in causal identification.
arXiv Detail & Related papers (2023-09-29T14:33:48Z) - Benchmarking Bayesian Causal Discovery Methods for Downstream Treatment
Effect Estimation [137.3520153445413]
A notable gap exists in the evaluation of causal discovery methods, where insufficient emphasis is placed on downstream inference.
We evaluate seven established baseline causal discovery methods including a newly proposed method based on GFlowNets.
The results of our study demonstrate that some of the algorithms studied are able to effectively capture a wide range of useful and diverse ATE modes.
arXiv Detail & Related papers (2023-07-11T02:58:10Z) - Causal Inference under Data Restrictions [0.0]
This dissertation focuses on modern causal inference under uncertainty and data restrictions.
It includes applications to neoadjuvant clinical trials, distributed data networks, and robust individualized decision making.
arXiv Detail & Related papers (2023-01-20T20:14:32Z) - Disentangled Counterfactual Recurrent Networks for Treatment Effect
Inference over Time [71.30985926640659]
We introduce the Disentangled Counterfactual Recurrent Network (DCRN), a sequence-to-sequence architecture that estimates treatment outcomes over time.
With an architecture that is completely inspired by the causal structure of treatment influence over time, we advance forecast accuracy and disease understanding.
We demonstrate that DCRN outperforms current state-of-the-art methods in forecasting treatment responses, on both real and simulated data.
arXiv Detail & Related papers (2021-12-07T16:40:28Z) - 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) - Stochastic Intervention for Causal Effect Estimation [7.015556609676951]
We propose a new propensity score and intervention effect estimator (SIE) to estimate intervention effect.
We also design a customized genetic algorithm specific to intervention effect (Ge-SIO) with the aim of providing causal evidence for decision making.
Our proposed measures and algorithms can achieve a significant performance lift in comparison with state-of-the-art baselines.
arXiv Detail & Related papers (2021-05-27T01:12:03Z) - Learning Continuous Treatment Policy and Bipartite Embeddings for
Matching with Heterogeneous Causal Effects [8.525061716196424]
Current methods make binary yes-or-no decisions based on the treatment effect of a single outcome dimension.
We propose to formulate the effectiveness of treatment as a parametrizable model, expanding to a multitude of treatment intensities and complexities.
We utilize deep learning to optimize the desired holistic metric space instead of predicting single-dimensional treatment counterfactual.
arXiv Detail & Related papers (2020-04-21T01:36:20Z) - 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.