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Found 23 papers, 7 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.
Mining the Factor Zoo: Estimation of Latent Factor Models with Sufficient Proxies
Latent factor model estimation typically relies on either using domain knowledge to manually pick several observed covariates as factor proxies, or purely conducting multivariate analysis such as principal component analysis.
Adaptive Semi-Supervised Inference for Optimal Treatment Decisions with Electronic Medical Record Data
Simulation studies are conducted to assess the empirical performance of the proposed method and to compare with a fully supervised method using only the labeled data.
On Learning and Testing of Counterfactual Fairness through Data Preprocessing
Machine learning has become more important in real-life decision-making but people are concerned about the ethical problems it may bring when used improperly.
Targeted Optimal Treatment Regime Learning Using Summary Statistics
We consider the problem of treatment regime estimation when the source and target populations may be heterogeneous, individual-level data is available from the source population, and only the summary information of covariates, such as moments, is accessible from the target population.
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.
A Probit Tensor Factorization Model For Relational Learning
A large number of models and algorithms have been proposed to perform link prediction, among which tensor factorization method has proven to achieve state-of-the-art performance in terms of computation efficiency and prediction accuracy.
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.
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.
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.
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.
Counterfactual Fairness through Data Preprocessing
Machine learning has become more important in real-life decision-making but people are concerned about the ethical problems it may bring when used improperly.
GraphCGAN: Convolutional Graph Neural Network with Generative Adversarial Networks
In a number of experiments on benchmark datasets, we show that the proposed GraphCGAN outperforms the baseline methods by a significant margin.
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.
Statistical Inference for Online Decision-Making: In a Contextual Bandit Setting
Based on the properties of the parameter estimators, we further show that the in-sample inverse propensity weighted value estimator is asymptotically normal.
Statistical Inference for Online Decision Making via Stochastic Gradient Descent
Focusing on the statistical inference of online decision making, we establish the asymptotic normality of the parameter estimator produced by our algorithm and the online inverse probability weighted value estimator we used to estimate the optimal value.
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.
Kernel Assisted Learning for Personalized Dose Finding
In this article, we propose a kernel assisted learning method for estimating the optimal individualized dose rule.
Does the Markov Decision Process Fit the Data: Testing for the Markov Property in Sequential Decision Making
The Markov assumption (MA) is fundamental to the empirical validity of reinforcement learning.
Implications from ASKAP Fast Radio Burst Statistics
Although there has recently been tremendous progress in studies of fast radio bursts (FRBs), the nature of their progenitors remains a mystery.
High Energy Astrophysical Phenomena
Post-Lasso Inference for High-Dimensional Regression
In this paper, we consider variable selection from a new perspective motivated by the frequently occurred phenomenon that relevant variables are not completely distinguishable from noise variables on the solution path.
Methodology
Robust Learning for Optimal Treatment Decision with NP-Dimensionality
In this paper, we propose a two-step estimation procedure for deriving the optimal treatment regime under NP dimensionality.
Sequential Advantage Selection for Optimal Treatment Regimes
Gunter et al. (2011) proposed S-score which characterizes the magnitude of qualitative interaction of each variable with treatment individually.
