Develops a method to estimate personalized treatment regimes from summary statistics.
problem Estimating optimal treatment regimes for a target population when individual-level data is unavailable.
method A weighting framework that tailors a treatment regime for the target population using summary statistics.
result Consistent and asymptotically normal estimator for optimal treatment regimes.
RL algorithms with medical integration improve personalized treatment recommendations.
problem Developing effective personalized treatment strategies for chronic diseases.
method Integrating medical knowledge into RL algorithms for DTR.
result Enhanced treatment recommendations with increased confidence.
Optimal treatment regime uses proxy variables to improve decision-making.
problem Insufficient covariates in observational data lead to confounding issues.
method Proximal causal inference framework and outcome/treatment confounding bridges.
result The proposed optimal treatment regime outperforms existing ones.
Develops methods for personalized treatment decisions in the presence of unmeasured factors.
problem Personalized treatment decisions in the presence of unmeasured confounding.
method Proximal learning approaches to estimate optimal individualized treatment regimes (ITRs).
result Established identification results for different classes of ITRs, improving decision-making value function.
Proposes a method to estimate personalized treatments from high-dimensional data.
problem Estimating individualized treatment regimes (ITRs) from high-dimensional covariates.
method Directly targets the contrast between potential outcomes, using dimension-reduced outcome-weighted learning.
result Achieves universal consistency, converging to the Bayes risk under mild conditions.
Proposes methods for learning optimal dynamic treatment regimes robust to unconfoundedness violations.
problem Estimating optimal dynamic treatment regimes using historical observational data when unconfoundedness is violated.
method Utilizes proximal causal inference framework to propose three nonparametric identification methods, a (K+1)-robust method, and establish a semiparametric efficiency bound.
result Establishes the (K+1)-robust method for learning optimal dynamic treatment regimes, validating its efficiency and multiple robustness through numerical experiments.
The vision for precision medicine is to use individual patient characteristics to inform a personalized treatment plan that leads to the best healthcare possible for each patient. Mobile technologies have an important role to play in this vision as they offer a means to monitor a patient's health status in real-time an…
Sensitivity analysis for individualized effects in OTRs with binary risk factors.
problem Addressing omitted confounding in individualized effects of OTRs.
method Simulation-based sensitivity analysis to simulate unmeasured confounders.
result Benchmarking the strength of omitted confounding for binary risk factors.
Method learns optimal treatment sequences from observational data.
problem Optimal dynamic treatment regimes for public policies and medical interventions.
method Doubly robust classification-based approach via backward induction.
result Achieves optimal convergence rate of n^(-1/2) for welfare regret.
The paper develops methods to estimate optimal treatment sequences under policy constraints.
problem Estimating the best sequence of treatments over multiple stages for individuals.
method Empirical welfare maximization approach, solving treatment assignment sequentially or simultaneously.
result Established convergence rates and upper bounds for estimation methods.
Proposes a new method for dynamic treatment regimes that improves sample efficiency and stability.
problem Challenges in estimating optimal treatments for individuals with dynamic decision-making stages.
method Focuses on prioritizing alignment between observed and optimal treatment trajectories across decision stages.
result Improves sample efficiency and stability of IPWE-based methods by relaxing the alignment requirement.
Method uses semi-supervised learning to estimate optimal treatment regimes from medical records.
problem Estimating optimal treatment regimes from electronic medical records.
method Imputation-based semi-supervised method using unlabeled data.
result Proposed method yields more efficient estimators of optimal treatment regimes.
A new algorithm learns optimal personalized treatment plans online with low regret.
problem Learning optimal dynamic treatment regimes in an online setting.
method Developed a novel algorithm balancing exploration and exploitation for rate-optimal regret.
result Guaranteed rate-optimal regret for linear transition and reward models.
A treatment regime is a function that maps individual patient information to a recommended treatment, hence explicitly incorporating the heterogeneity in need for treatment across individuals. Patient responses are dichotomous and can be predicted through an unknown relationship that depends on the patient information …
Optimal treatment regimes (OTR) are individualised treatment assignment strategies that identify a medical treatment as optimal given all background information available on the individual. We discuss Bayes optimal treatment regimes estimated using a loss function defined on the bivariate distribution of dichotomous po…
A neural framework corrects bias in estimating individual treatment effects.
problem Estimating individual treatment effects from observational data.
method An anchored neural architecture and precision-corrected intersection-bound inference.
result Corrected bias and maintained nominal coverage in high-dimensional settings.
New method estimates optimal personalized treatment rules from mixed data sources.
problem Combining RCT and observational data for personalized treatment rules.
method Doubly robust estimator for value function, maximizing within pre-specified ITR class.
result Consistent and asymptotically normal optimal value estimator with N−1/3 rate of convergence. The field of precision medicine aims to tailor treatment based on patient-specific factors in a reproducible way. To this end, estimating an optimal individualized treatment regime (ITR) that recommends treatment decisions based on patient characteristics to maximize the mean of a pre-specified outcome is of particular…
Proposes pT-Learning for optimal dynamic treatment regimes in mHealth.
problem Challenges in learning optimal dynamic treatment regimes with large intervention options and infinite time horizon.
method Proximal Temporal consistency Learning (pT-Learning) framework for adaptively adjusting between deterministic and stochastic policies.
result Minimax estimator avoids double sampling issue and can incorporate off-policy data.
Variable selection for optimal treatment regime in a clinical trial or an observational study is getting more attention. Most existing variable selection techniques focused on selecting variables that are important for prediction, therefore some variables that are poor in prediction but are critical for decision-making…
Q-learning adapted to find near-equivalent treatment strategies.
problem Finding optimal treatment sequences in dynamic treatment regimes.
method Introducing a worst-value tolerance criterion to find sets of near-equivalent policies.
result Constructs families of near-equivalent treatment strategies.
TV-SurvCaus improves causal inference for dynamic treatments in survival analysis.
problem Estimating causal effects of time-varying treatments on survival outcomes.
method Representation balancing techniques extended to time-varying treatment regimes with survival outcomes.
result TV-SurvCaus outperforms existing methods in estimating individualized treatment effects with time-varying covariates and treatments.
LUQ-Learning adapts Q-learning for healthcare decisions considering patient preferences.
problem Optimizing treatment decisions for multivariate outcomes based on individual preferences.
method Latent Utility Q-Learning (LUQ-Learning) framework that adapts Q-learning for composite outcomes.
result LUQ-Learning achieves highly competitive performance compared to alternative methods in simulations.
POLAR optimizes treatment strategies in dynamic settings with statistical guarantees.
problem Optimizing sequential decisions in dynamic treatment regimes with robustness and statistical guarantees.
method Pessimistic model-based approach estimating transition dynamics and incorporating uncertainty penalties.
result Offers statistical and computational guarantees, including finite-sample bounds on policy suboptimality.
Proposes a new method to estimate optimal treatment regimes in the presence of endogeneity.
problem Estimating optimal treatment regimes under endogeneity in observational studies or randomized trials.
method Semiparametric instrumental variable approach with binary instrumental variable.
result Identification and estimation of optimal treatment regimes under endogeneity without direct compliance information.
G-Net uses deep learning for complex counterfactual outcome prediction.
problem Estimating counterfactual outcomes under dynamic treatment strategies.
method G-Net is a sequential deep learning framework for G-computation.
result G-Net can handle complex temporal data and provide accurate treatment effects.
New method for robust inference on optimal treatment regimes without model specification.
problem Inference on optimal treatment regimes without specifying outcome regression models.
method Smoothed robust estimator and resampling-based inference.
result Asymptotic normal distribution and accurate inference for optimal treatment regimes.
New framework estimates treatment effects in extreme data.
problem Hindered by unavailability of counterfactual outcomes and rarity of extreme data.
method Proposes a new framework based on extreme value theory.
result Quantifies treatment effects using tail decay rates of potential outcomes.
Dynamic treatment regimes are of growing interest across the clinical sciences as these regimes provide one way to operationalize and thus inform sequential personalized clinical decision making. A dynamic treatment regime is a sequence of decision rules, with a decision rule per stage of clinical intervention; each de…
Heteroskedasticity biases uplift model rankings, leading to inefficient treatment allocation.
problem Bias in uplift model rankings due to heteroskedasticity.
method Theoretical analysis and simulation on real-world data.
result Heteroskedasticity can cause individuals with high treatment effects to be ranked at the bottom, leading to inefficient treatment allocation.
The estimation of optimal treatment regimes is of considerable interest to precision medicine. In this work, we propose a causal k-nearest neighbor method to estimate the optimal treatment regime. The method roots in the framework of causal inference, and estimates the causal treatment effects within the nearest neig…
New method evaluates personalized treatment in critical care, robust to death.
problem Truncation by death in critical care makes traditional DTR evaluation ineffective.
method Principal stratification-based approach, focusing on always-survivor value function, with a semiparametrically efficient, multiply robust estimator.
result Demonstrates robustness and efficiency of the method for personalized treatment optimization.
The paper compares methods for estimating individual treatment effects.
problem Estimating the optimal treatment effect for each individual.
method Comparison of machine learning methods for individual treatment effect estimation.
result Combination of Logistic Regression and Difference Score method, as well as Uplift Random Forest method, provides the best prediction accuracy.
Proposes a new model for estimating individual treatment effects.
problem Estimating individual treatment effects from observational data is challenging.
method Integrates diffusion modeling and conformal inference with propensity score and covariate approximation.
result Establishes rigorous theoretical guarantees and demonstrates competitive performance.
New method for finding optimal treatment regimes in medical settings with time-varying unobserved factors.
problem Finding optimal treatment regimes in medical settings with time-varying unobserved factors.
method Extend Dynamic Treatment Regimes (DTRs) to Ambiguous Dynamic Treatment Regimes (ADTRs), connect to Ambiguous Partially Observable Mark Decision Processes (APOMDPs), and develop Reinforcement Learning methods.
result Established theoretical results for learning methods, including consistency and asymptotic normality.
The paper clarifies the distinction between CATE and ITE under ignorability assumptions.
problem Confusion between CATE and ITE hinders personalized effect estimation.
method Clarifies the distinction between CATE and ITE under ignorability assumptions.
result CATE and ITE are not necessarily the same under ignorability assumptions.
Develops methods for near-optimal personalized treatment recommendations.
problem Assigning optimal treatments to patients based on individual characteristics.
method Outcome weighted learning framework to estimate near-optimal alternative individualized treatment recommendations (A-ITR).
result Consistency of proposed methods and upper bound for risk between optimal and estimated recommendations.
Estimating treatment effects in time series with hidden confounding.
problem Estimating treatment effects in time series with hidden confounding.
method A neural framework that learns individual-level counterfactuals and flexible matching procedures.
result Improves counterfactual estimation under latent bias.
The causal effect of a treatment can vary from person to person based on their individual characteristics and predispositions. Mining for patterns of individual-level effect differences, a problem known as heterogeneous treatment effect estimation, has many important applications, from precision medicine to recommender…
GraphITE estimates individual effects of graph-structured treatments.
problem Estimating individual effects of complex treatment structures.
method Graph neural networks and Hilbert-Schmidt Independence Criterion regularization.
result GraphITE outperforms baselines in estimating treatment effects for large numbers of treatments.
Estimates individual treatment effects using gradient interpolation and kernel smoothing.
problem Estimating individualized continuous treatment effects in observational data.
method Augment training data with independently sampled treatments and inferred counterfactual outcomes using gradient interpolation and kernel smoothing.
result Our method outperforms state-of-the-art methods on counterfactual estimation error.
The paper tackles adaptive targeting in networks with interference effects.
problem Adaptive targeting under network interference in a bandit setting.
method Linear model in a sparse regime, analyzing different levels of knowledge of the interference structure.
result Unified view of how knowledge of the interference structure affects online learning efficiency.
TCFimt forecasts causal effects of multiple interventions from individual data.
problem Estimating causal effects of temporal multi-interventions from individual data.
method TCFimt uses adversarial tasks in seq2seq framework to alleviate bias and contrastive learning to decouple effects.
result TCFimt outperforms state-of-the-art methods in predicting future outcomes and choosing optimal treatments.
LI-ITR combines flexible ML with interpretable approximations for personalized treatment rules.
problem Combining flexibility and interpretability in personalized treatment rules.
method Uses variational autoencoders and a mixture of interpretable experts.
result Accurately recovers true local coefficients and optimal treatment strategies.
Paper tackles estimating individual treatment effects from observational data.
problem Estimating the difference between outcomes with and without treatment from single observation.
method Formulated as inference from hidden variables, uses a model of four causal populations, proposes ECM algorithm.
result ECM algorithm provides better performance compared to baseline methods on synthetic and real-world data.
Proposes a new decision rule for continuous treatments.
problem Developing personalized treatment recommendations for continuous treatments.
method Jump interval-learning method to estimate conditional mean of outcomes.
result Optimal interval-valued decision rule (I2DR) for continuous treatments.
Deep Bayesian models estimate causal effects for dynamic treatment regimes over long follow-up times.
problem Challenges in causal effect estimation for dynamic treatment regimes with long follow-up times.
method Combining outcome regression models with deep Bayesian models for high-dimensional features.
result Stable and accurate dynamic causal effect estimation from observational data, especially with long-term follow-up.
Develops methods to learn optimal treatment regimes using causal tree methods.
problem Lack of methods for estimating treatment effects and handling complex patient data.
method Causal tree and causal forest methods for estimating heterogeneous treatment effects.
result Outperforms state-of-the-art baselines in cumulative regret and percentage of optimal decisions.