Pessimistic Causal Reinforcement Learning with Mediators for Confounded Offline Data Analysis

Основні поняття

Proposing a novel policy learning algorithm, PESCAL, to address confounding bias and distributional shift in offline data.

Анотація

In the real world, leveraging large observational datasets for policy learning is crucial due to limitations in randomized experiments. Existing offline RL methods face challenges like unconfoundedness and positivity assumptions not holding in observational data contexts. The proposed PESCAL algorithm incorporates a mediator variable based on the front-door criterion to remove confounding bias and adopts the pessimistic principle to address distributional shift. By learning a lower bound of the mediator distribution function, it simplifies the algorithm and provides theoretical guarantees. The algorithm is demonstrated through simulations and real-world experiments using offline datasets from ride-hailing platforms.

Статистика

"datasets collected from randomized experiments are often limited in size due to time or budget constraints."
"the behavior policy is close to deterministic, certain state-action pairs are explored less frequently in the offline data."
"the magnitude of the treatment effect typically falls within a modest range of 0.5% to 2%."

Цитати

"Incorporating auxiliary variables that mediate the effect of actions on system dynamics simplifies our algorithm."
"Our proposal addresses both confounding bias and distributional shift issues in offline policy learning."

Ключові висновки, отримані з

Pessimistic Causal Reinforcement Learning with Mediators for Confounded Offline Data

by Danyang Wang... о arxiv.org 03-19-2024

https://arxiv.org/pdf/2403.11841.pdf

Глибші Запити

How can PESCAL be adapted for other applications beyond ride-sharing platforms

PESCAL can be adapted for other applications beyond ride-sharing platforms by modifying the specific variables and dynamics of the problem. The key concept of using a mediator variable based on the front-door criterion to remove confounding bias can be applied in various domains where unmeasured confounders exist. For example, in healthcare settings, where patient outcomes may be influenced by both observed and unobserved factors, PESCAL could help identify optimal treatment strategies while accounting for these hidden variables. Similarly, in finance or marketing, where external factors can impact decision-making processes, incorporating mediators into causal reinforcement learning algorithms like PESCAL could lead to more robust policy recommendations.

What counterarguments exist against using mediators in causal reinforcement learning

Counterarguments against using mediators in causal reinforcement learning include concerns about model complexity and interpretability. Introducing a mediator variable adds an additional layer of abstraction to the modeling process, which may make it harder to understand how actions directly influence outcomes. Moreover, there is a risk of introducing bias if the mediator is not correctly identified or if its relationship with actions and outcomes is misinterpreted. Additionally, relying too heavily on mediators could lead to oversimplification of complex causal relationships within a system.

How might uncertainty quantification impact decision-making in real-world scenarios

Uncertainty quantification plays a crucial role in decision-making in real-world scenarios by providing insights into the reliability and robustness of estimated values or policies. In contexts like healthcare or finance, where decisions have significant consequences, understanding uncertainty helps stakeholders assess risks and potential outcomes more accurately. For instance, uncertainty quantification can guide clinicians in choosing treatment options with varying levels of confidence based on available data or assist financial analysts in making investment decisions considering market volatility and unpredictability. By acknowledging uncertainty explicitly through methods like probabilistic modeling or sensitivity analysis, decision-makers can make more informed choices that account for potential variability and mitigate adverse effects from unforeseen circumstances.