Search Results for author:
Found 15 papers, 6 papers with code
On Validation and Planning of An Optimal Decision Rule with Application in Healthcare Studies
Estimation of optimal decision rules (ODR) has been extensively investigated recently, however, at present, no testing procedure is proposed to verify whether these ODRs are significantly better than the naive decision rule that always assigning individuals to a fixed treatment option.
Is Knowledge All Large Language Models Needed for Causal Reasoning?
This paper explores the causal reasoning of large language models (LLMs) to enhance their interpretability and reliability in advancing artificial intelligence.
Towards Trustworthy Explanation: On Causal Rationalization
With recent advances in natural language processing, rationalization becomes an essential self-explaining diagram to disentangle the black box by selecting a subset of input texts to account for the major variation in prediction.
Sequential Knockoffs for Variable Selection in Reinforcement Learning
In real-world applications of reinforcement learning, it is often challenging to obtain a state representation that is parsimonious and satisfies the Markov property without prior knowledge.
On Heterogeneous Treatment Effects in Heterogeneous Causal Graphs
To characterize this heterogeneity, we first conceptualize heterogeneous causal graphs (HCGs) by generalizing the causal graphical model with confounder-based interactions and multiple mediators.
Heterogeneous Synthetic Learner for Panel Data
In the new era of personalization, learning the heterogeneous treatment effect (HTE) becomes an inevitable trend with numerous applications.
Jump Interval-Learning for Individualized Decision Making
To derive an optimal I2DR, our jump interval-learning method estimates the conditional mean of the outcome given the treatment and the covariates via jump penalized regression, and derives the corresponding optimal I2DR based on the estimated outcome regression function.
Doubly Robust Interval Estimation for Optimal Policy Evaluation in Online Learning
We use this probability to conduct valid inference on the online conditional mean estimator under each action and develop the doubly robust interval estimation (DREAM) method to infer the value under the estimated optimal policy in online learning.
CAPITAL: Optimal Subgroup Identification via Constrained Policy Tree Search
In this paper, we present an optimal subgroup selection rule (SSR) that maximizes the number of selected patients, and in the meantime, achieves the pre-specified clinically meaningful mean outcome, such as the average treatment effect.
Periodic-GP: Learning Periodic World with Gaussian Process Bandits
We consider the sequential decision optimization on the periodic environment, that occurs in a wide variety of real-world applications when the data involves seasonality, such as the daily demand of drivers in ride-sharing and dynamic traffic patterns in transportation.
GEAR: On Optimal Decision Making with Auxiliary Data
We propose an auGmented inverse propensity weighted Experimental and Auxiliary sample-based decision Rule (GEAR) by maximizing the augmented inverse propensity weighted value estimator over a class of decision rules using the experimental sample, with the primary outcome being imputed based on the auxiliary sample.
Calibrated Optimal Decision Making with Multiple Data Sources and Limited Outcome
We consider the optimal decision-making problem in a primary sample of interest with multiple auxiliary sources available.
ANOCE: Analysis of Causal Effects with Multiple Mediators via Constrained Structural Learning
Under a general causal graph, the exposure may have a direct effect on the outcome and also an indirect effect regulated by a set of mediators.
Deep Jump Learning for Off-Policy Evaluation in Continuous Treatment Settings
To handle continuous treatments, we develop a novel estimation method for OPE using deep jump learning.
Deep Jump Q-Evaluation for Offline Policy Evaluation in Continuous Action Space
To handle continuous action space, we develop a brand-new deep jump Q-evaluation method for OPE.
