Estimates cost savings from early cancer diagnosis.
problem Improving early cancer diagnosis to reduce treatment costs.
method Combining published cancer treatment cost estimates by stage with incidence rates by stage at diagnosis, and extrapolating to other cancer sites.
result Estimates U.S. national annual treatment cost-savings from early cancer diagnosis in the trillions.
Extends uplift modeling for multiple cost treatments.
problem Optimizing interventions with multiple treatment groups and varying costs.
method Extended standard uplift models to support multiple treatment groups with different costs.
result Improved performance of models in synthetic and real data.
Optimizes biomarker selection for cost-effective treatment rules.
problem Incorporating multiple biomarkers in treatment selection rules can be costly and reduce model performance.
method Developed procedures for estimating linear and nonlinear combinations of biomarkers using 0-norm penalized weighted classification.
result Demonstrated the importance of feature selection and marker cost in treatment selection rules.
Extends expected value framework for cost-sensitive causal decision-making.
problem Optimizing operational decision-making with cost-sensitive causal classification.
method Introduces a cost-sensitive decision boundary based on estimated individual treatment effects, positive outcome probability, and cost parameters.
result Effective in maximizing expected causal profit, outperforming cost-insensitive ranking approach.
The paper develops a method to optimize individualized treatment rules for cost-effectiveness.
problem Developing cost-effective individualized treatment rules for healthcare policy.
method Using conditional random forest and net-monetary-benefit (NMB) to estimate optimal CE-ITR.
result The approach optimizes healthcare resource allocation by maximizing health gains and minimizing costs.
Decision makers, such as doctors and judges, make crucial decisions such as recommending treatments to patients, and granting bails to defendants on a daily basis. Such decisions typically involve weighting the potential benefits of taking an action against the costs involved. In this work, we aim to automate this task…
Decision makers, such as doctors and judges, make crucial decisions such as recommending treatments to patients, and granting bails to defendants on a daily basis. Such decisions typically involve weighting the potential benefits of taking an action against the costs involved. In this work, we aim to automate this task…
Supervised randomization makes randomized experiments more cost-effective for uplift modeling.
problem Costly randomized experiments for uplift modeling.
method Integrates existing scoring models into randomized trials to target relevant customers while correcting for selection bias.
result Cost-efficient data collection under supervised randomization with competitive uplift model performance.
Optimizes user marketing campaigns to balance cost and effectiveness.
problem Lack of methods to optimize marketing campaigns considering cost and effectiveness.
method Proposes a treatment effect optimization algorithm using deep learning to balance cost and effectiveness.
result Demonstrates superior performance in cost-efficiency and real-world business value.
Optimizes staggered treatment rollouts to minimize cost and error.
problem Efficiently scheduling treatment initiation times for staggered rollouts.
method Non-adaptive and adaptive experimental designs, including a near-optimal solution for non-adaptive cases and a new algorithm for adaptive cases.
result Reduces experiment cost by over 50% compared to static design benchmarks.
Proposes TLRNet for estimating treatment effects from observational data.
problem Estimating individual treatment effects from observational data.
method Deep neural network with pseudo-single learner structure.
result Acceptable results obtained using one estimator for two treatment groups.
Crowdsourced reinforcement learning optimizes knee replacement pathway, reducing costs.
problem Optimizing the sequential decision process for knee replacement surgery.
method Reinforcement learning, value iteration, state compression, kernel representation, cross validation.
result Optimized policy reduces overall cost by 7% and excessive cost premium by 33%.
XTNet estimates complex cross-treatment effects in multi-category, multi-valued settings.
problem Challenges in estimating causal effects for multi-category, multi-valued treatments.
method Dynamic Neural Masking for capturing treatment interactions without restrictive assumptions.
result XTNet consistently outperforms state-of-the-art baselines in multi-category, multi-valued treatment effect estimation.
Proposes a new method to estimate continuous treatment policies and match treatments effectively.
problem Current methods struggle with continuous treatment policies and complex matching.
method Formulates treatment effectiveness as a parametrizable model, using deep learning for optimization.
result Significant improvement in treatment effectiveness and matching efficiency.
Efficiently find near-optimal medical treatments with less trial and error.
problem Finding effective medical treatments through trial and error.
method Formalizes the problem, uses a causal inference framework, and proposes model-based dynamic programming and greedy algorithms.
result Our methods compare favorably to model-free reinforcement learning, offering a more transparent trade-off between search time and treatment efficacy.
New method uses latent variables to estimate treatment effects from single-arm trials.
problem Estimating treatment effects from single-arm trials due to lack of external control groups.
method Latent-variable modeling with amortized variational inference for patient matching and direct effect estimation.
result Improved performance in direct treatment effect estimation and effect estimation via patient matching compared to previous methods.
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 …
Low-cost device diagnoses diseases using deep learning.
problem Limited healthcare access and variable disease properties.
method Deep Neural Networks (DNNs) and Convolutional Neural Networks (CNNs) on a Raspberry Pi Zero.
result Achieves 90% accuracy for symptom-based diagnoses and 91% for visual skin diseases.
Improves A/B testing by detecting minor treatment effects.
problem Challenges in identifying small average treatment effects.
method Maximum probability-driven two-armed bandit (TAB) process with weighted mean volatility statistic.
result Significant improvement in A/B testing with reduced experimental costs.
Bayesian model for cost-effectiveness analysis with subgroup discovery.
problem Statistical challenges in cost-effectiveness analysis, especially with non-random treatment assignment and censored data.
method Developed a nonparametric Bayesian model using Dirichlet and Gamma processes to estimate cost-survival distributions and identify cost-effectiveness subgroups.
result Identified and estimated policy-relevant causal CEA estimands using a Bayesian nonparametric g-computation procedure.
The paper explores fair regression and classification under demographic parity constraints.
problem Ensuring fairness in regression and classification models under demographic parity constraints.
method Characterizes the optimal fair regression function using a barycenter problem with optimal transport costs and studies the connection between fair classification and regression.
result The optimal fair regression function is derived from the solution to a barycenter problem with optimal transport costs, and the optimal fair cost-sensitive classifiers can be derived by applying thresholds to this function.
Paper derives policy rules from observational data for hepatitis C treatment.
problem Improving treatment guidelines for HIV/HCV co-infected patients.
method Weighted K-means algorithm for estimating CATEs, decision tree implementation.
result Identifies a subgroup with high spontaneous HCV clearance rate.
Proposes a new method to estimate individual treatment effects using unlabeled data.
problem Difficult estimation of individual treatment effects due to high costs of intervention studies.
method Combines causal inference matching and semi-supervised learning label propagation.
result Demonstrates successful mitigation of data scarcity in ITE estimation.
Medical deconfounder uses EHRs to estimate treatment effects without confounders.
problem Bias in assessing treatment effects from EHRs due to unobserved confounders.
method Develops a machine learning algorithm (medical deconfounder) to adjust for confounders.
result Medical deconfounder produces more accurate treatment effect estimates and identifies effective medications.
New method uses Markov chains for cost-optimal healthcare monitoring.
problem Optimizing healthcare monitoring protocols for patient health characteristics.
method Adapts Markov chain approach to account for random shift sizes, repairs, and time intervals.
result Optimal parameters can differ from traditional medical protocols, showing new insights.
DSL estimates heterogeneous treatment effects over time in survival settings.
problem Complicated by right censoring and time-varying treatment effects.
method Deep survival learner (DSL) for estimating CATEs over a clinically relevant time spectrum.
result DSL reveals heterogeneity in perioperative chemotherapy effects over time.
Proposes a framework to reconcile policy learning and profit maximization in CATE estimation.
problem Aligning CATE estimation with profit maximization for optimal customer treatment decisions.
method Optimizes a novel objective function that concentrates learning capacity near the decision boundary, ensuring consistency with the original profit function.
result Consistent CATE estimates can be recovered from existing profit-maximization pipelines, allowing firms to navigate the trade-off between accuracy and profit.
Comment on entropy learning for dynamic treatment regimes.
problem Evaluating dynamic treatment regimes using entropy loss.
method Optimization-based alternative to IPW estimate.
result Suggests optimization-based approach for evaluation.
Proposes TPIS for early and low-cost TB vs. pneumonia diagnosis.
problem Challenges in differentiating TB from pneumonia.
method Two-step decision support system with stacked ensemble classifiers.
result TPIS outperforms other methods in early and final diagnosis.
The paper addresses Qini curve estimation under clustered network interference.
problem Qini curves can be biased when interference is ignored in clustered network settings.
method Proposes three estimation strategies for clustered network interference.
result Identifies the most appropriate approach based on bias-variance trade-offs.
In the modern healthcare system, rapidly expanding costs/complexity, the growing myriad of treatment options, and exploding information streams that often do not effectively reach the front lines hinder the ability to choose optimal treatment decisions over time. The goal in this paper is to develop a general purpose (…
Method finds optimal binary classification rules under weighted misclassification loss.
problem Optimal binary classification rules for resource-limited settings with cost-sensitive decisions.
method Ensemble learning to derive prediction scores and associated thresholds minimizing weighted misclassification loss.
result Jointly derived score and threshold outperforms methods that derive score first and threshold second.
We show that different rates should be used for borrowing and discount rates, and that the risk-free rate should be used for discounting when assessing and comparing the cost of energy accross diffferent producers and technologies, on the example of photovoltaics. Recent quantitative models using the same rate for borr…
Expands causal clustering framework with hierarchical and density-based methods.
problem Identifying heterogeneous treatment effects in unknown subgroup structure.
method Integrates hierarchical and density-based clustering algorithms into causal k-means clustering.
result Plug-in estimators for causal clustering are simple and readily implementable.
A hybrid algorithm fuses significance-based splitting with honest sample-splitting for estimating heterogeneous treatment effects.
problem Estimating heterogeneous treatment effects while maintaining valid inference.
method Significance-first splitting using a squared t-statistic for treatment imes side interaction. result Achieves approximately 90% CI coverage at the 90% nominal level across various synthetic designs and datasets.
The paper develops a causal machine learning framework to optimize aid allocation.
problem Optimizing aid allocation to reduce new HIV infections in poor countries.
method The framework uses a balancing autoencoder, counterfactual generator, and inference model to predict heterogeneous treatment effects.
result The framework predicts a reduction of up to 3.3% in new HIV infections, saving 50,000 lives.
Study benchmarks contextual bandit algorithms for precision oncology using in vitro data.
problem Designing effective protocols for individual treatment assignment in precision oncology.
method Proposed a benchmark dataset of in vitro drug responses to evaluate contextual bandit algorithms.
result Bayesian bandit algorithms performed better than a rule-based baseline in minimizing regret.
Meta-Router optimizes LLM selection using gold-standard and preference-based data.
problem Training a high-quality LLM router with combined data sources is challenging due to bias and scarcity.
method Developed an integrative causal router training framework to correct bias and improve routing accuracy.
result Our approach delivers more accurate routing and improves the trade-off between cost and quality.
Efficient adjustment sets found for cost-minimized causal estimations.
problem Estimating interventional means with minimum cost in causal graphical models.
method Defined cost-adjustment sets, constructed flow networks, and used maximum flow algorithms.
result Minimum cost optimal adjustment sets exist and can be found efficiently.
Proposes K-Fold Causal BART for improved CATE estimation.
problem Improving estimation of Conditional Average Treatment Effects (CATE).
method K-Fold Causal Bayesian Additive Regression Trees (K-Fold Causal BART).
result K-Fold Causal BART is not state-of-the-art for ATE and CATE estimation in the IHDP dataset, but provides insights into model robustness and evaluation methods.
Study predicts individual treatment effects in ride-sharing competitions.
problem Understanding how team competitions affect individual drivers' outcomes.
method Analyzed data from 500 competitions, built machine learning models.
result Reduced out-sample prediction error by over 24%.
Synth-validation selects the best causal inference method for a dataset.
problem Selecting the best causal inference method for a given dataset.
method Cross-validation analogy applied to causal inference methods.
result Synth-validation lowers expected estimation error.
LLM-based trading systems vary in execution realism and reproducibility.
problem LLM-based trading systems vary in execution realism and reproducibility.
method Reproducibility audit of 30 trade-relevant primary studies.
result LLM-based trading systems vary in execution realism and reproducibility.
Algorithm learns interference network and optimizes treatment allocation for unknown network effects.
problem Adaptive experimentation under unknown network interference.
method Thompson sampling algorithm with Gibbs sampler for joint learning of interference network and treatment allocation.
result Proves a Bayesian regret bound and achieves sublinear regret in real-world applications.
Causal forests use honesty to reduce overfitting, but it can also reduce accuracy, especially with large datasets.
problem Causal forests' honesty can reduce accuracy of individual treatment effects.
method Using honest estimation to divide data into two samples, one for subgroup definition and another for effect estimation.
result Honest estimation can reduce accuracy by requiring 27% more data to match performance of non-honest models.
The paper provides bounds and methods for estimating causal effects from observational data.
problem Estimating individual-level causal effects from non-experimental data.
method Generalization bounds, sample re-weighting, representation learning algorithms.
result The proposed methods reduce treatment group distances and improve estimation accuracy.
Statistical test evaluates if personalizing interventions is cost-effective.
problem Balancing the benefits of personalizing interventions with their potential costs.
method Developed a statistical hypothesis test to assess the performance of personalized interventions.
result The test shows that personalized interventions can outperform standard approaches under certain conditions.
A statistical description and model of individual healthcare expenditures in the US has been developed for measuring value in healthcare. We find evidence that healthcare expenditures are quantifiable as an infusion-diffusion process, which can be thought of intuitively as a steady change in the intensity of treatment …