Three approaches learn personalized treatment policies for UTI patients.
problem Learning optimal treatment policies in multiobjective settings with fully observed outcomes.
method Indirect and direct approaches using predictive models and without intermediate models.
result All approaches outperform clinicians in achieving better performance on all outcomes and trade-offs.
The paper proposes a method to estimate treatment effects using surrogates when primary outcomes are missing.
problem Missing primary outcomes in causal inference applications can lead to biased estimates.
method Doubly robust method that uses both labeled and unlabeled data, incorporating surrogates.
result The proposed estimator is asymptotically normal and has improved variance compared to methods using only labeled data.
Tests whether a treatment's effect is fully mediated by observed outcomes and identifies causal mechanisms.
problem Understanding how a treatment affects an outcome through intermediate variables.
method Proposes a test to evaluate full mediation and causal mechanism identification, extending to non-randomly assigned treatments.
result A conditionally random treatment is conditionally independent of the outcome given mediators and covariates if full mediation and causal mechanism identification hold.
Confounding biases recommender systems, even when data seems fully observed.
problem Unmeasured features influencing both treatment and outcome in recommender systems.
method Illustrations and simulation studies showing how common practices introduce confounding.
result Standard recommender system practices can introduce confounding, reducing performance.
SurvFM-RMST converts survival outcomes into pseudo-observation targets for tabular models.
problem Right-censored follow-up prevents direct use of survival labels in tabular patient data.
method SurvFM-RMST framework that converts survival outcomes into jackknife pseudo-observation targets for restricted mean survival time.
result SurvFM-RMST accurately recovered restricted event-free time in simulations and outperformed naive targets in static datasets.
Improves matrix completion by exploiting biased observation patterns.
problem Matrix completion with biased observation patterns.
method Mask Nearest Neighbor (MNN) algorithm: two-stage process.
result MNN achieves competitive performance with 28x smaller mean squared error.
We study a model of wealth dynamics [Bouchaud and Mézard 2000, \emph{Physica A} \textbf{282}, 536] which mimics transactions among economic agents. The outcomes of the model are shown to depend strongly on the topological properties of the underlying transaction network. The extreme cases of a fully connected and a ful…
Study uses surrogate data to improve treatment effect estimation with scarce outcome data.
problem Limited outcome data hinders estimating treatment effects.
method Uses abundant surrogate data to estimate treatment effects without stringent assumptions.
result Improves precision of treatment effect estimation.
The paper explores methods to better estimate treatment effects by leveraging shared structure in potential outcomes.
problem Estimating treatment effects when outcomes may vary widely and existing methods often assume heterogeneity.
method Investigates and compares three learning strategies: regularization, reparametrization, and a multi-task architecture.
result All three approaches improve upon existing baselines, providing insights into their relative strengths.
Consider the case that one observes a single time-series, where at each time t one observes a data record O(t) involving treatment nodes A(t), possible covariates L(t) and an outcome node Y(t). The data record at time t carries information for an (potentially causal) effect of the treatment A(t) on the outcome Y(t), in…
PROWL uses robust reward estimates to improve ITR selection.
problem Reward uncertainty in ITR estimation leads to inflated performance.
method PAC-Bayesian framework with reward uncertainty certificates.
result PROWL achieves better robust treatment regime estimation.
Paper improves feature selection for predicting outcomes from observational data.
problem Feature selection for post-intervention outcome prediction from pre-intervention variables in healthcare settings.
method Extends Markov boundary concept to treatment-outcome pairs, uses observational and experimental data.
result Combining observational and experimental data improves feature selection and effect estimation.
Estimates counterfactual outcomes linking observed and unobserved data.
problem Estimating expected counterfactual outcomes for individuals.
method Introduces retrospective counterfactual estimators and prediction intervals linking observed and unobserved outcomes.
result Retrospective counterfactual estimators and prediction intervals asymptotically satisfy valid coverage under standard causal assumptions.
New method for valid prediction intervals with coarsened data.
problem Handling missing data and censored outcomes in training samples.
method Multiply robust conformal risk control with semiparametric theory.
result Stronger coverage properties under covariate shift.
The paper automates policy learning for nonlinear welfare criteria using machine learning and debiasing techniques.
problem Learning optimal policies from observational data with nonlinear welfare criteria.
method Modeling a nonlinear welfare criterion with a utility function, estimating propensity scores with machine learning, and using sieve approximations and cross-validation for model selection.
result The proposed policy learning method satisfies oracle inequalities, providing theoretical guarantees on performance.
We consider the recovery of regression coefficients, denoted by β0, for a single index model (SIM) relating a binary outcome Y to a set of possibly high dimensional covariates X, based on a large but 'unlabeled' dataset U, with Y never observed. On U, we fully ob…
PO-Flow models potential and counterfactual outcomes for personalized treatment decisions.
problem Predicting individualized treatment effects from observational data.
method Continuous normalizing flow (CNF) framework for causal inference.
result Unified approach to potential outcome prediction, treatment effect estimation, and counterfactual prediction.
CCN estimates full potential outcome distributions without restrictive assumptions.
problem Estimating CATE is insufficient; full potential outcome distributions provide greater insights.
method Collaborating Causal Networks (CCN) learns full potential outcome distributions without restrictive assumptions.
result CCN learns distributions that asymptotically capture true potential outcome distributions.
The paper bounds and identifies joint probabilities in causal inference with monotonicity assumptions.
problem Bounding and identifying joint probabilities of potential outcomes and observed variables under monotonicity assumptions.
method Proposes new families of monotonicity assumptions, formulates bounding problem as linear programming, introduces new monotonicity assumption for identification.
result Validated methods through numerical experiments and applied to real-world datasets.
DEBIAS learns causal effects from psychiatric longitudinal data by optimizing outcome weights.
problem Causal inference challenges in psychiatric longitudinal data due to symptom heterogeneity and latent confounding.
method DEBIAS algorithm that optimizes outcome weights to maximize durable treatment effects and minimize confounding.
result DEBIAS consistently outperforms state-of-the-art methods in recovering causal effects for clinically interpretable composite outcomes.
The paper develops a method to learn robust decision policies from observational data, reducing high-cost outcomes.
problem Learning safe decision policies from observational data with high-risk outcomes.
method Develops a method to learn policies that reduce high-cost outcomes, valid under finite samples and uneven feature overlap.
result Validates the method with real and synthetic data, providing statistical bounds on decision costs.
The paper tackles long-term treatment effects with persistent confounders using sequential short-term outcomes.
problem Estimating long-term treatment effects with persistent unmeasured confounders.
method Exploiting the sequential structure of short-term outcomes, the paper develops three novel identification strategies and corresponding estimators.
result The proposed methods outperform existing approaches in handling persistent confounders.
Study uses remotely sensed data to infer economic outcomes in experiments and quasi-experiments.
problem Imperfect measurement of economic outcomes by remotely sensed variables.
method Combines experimental and observational data to identify causal parameters, using satellite imagery and mobile phone activity.
result Developed a robust method for n^{-1/2} inference that does not restrict remotely sensed variable processing algorithms.
We propose a novel approach for inferring the individualized causal effects of a treatment (intervention) from observational data. Our approach conceptualizes causal inference as a multitask learning problem; we model a subject's potential outcomes using a deep multitask network with a set of shared layers among the fa…
Combining experimental and observational data for long-term causal effects.
problem Estimating causal effects of treatment on long-term outcomes using mixed data types.
method Three approaches for fusing experimental and observational data: equal confounding, shared confounder, and proxy variables.
result Developed estimators for each approach and analyzed their robustness.
DONUT improves treatment effect estimation by enforcing orthogonality constraints.
problem Estimating treatment effects from observational data is challenging due to unobserved outcomes.
method DONUT uses a regularization framework that formalizes unconfoundedness as orthogonality, leading to deep orthogonal networks.
result DONUT outperforms state-of-the-art methods in estimating average treatment effects.
The paper proposes an evolution-based approach to estimate causal effects in interference networks without fully observing the network structure.
problem Estimating causal effects in complex systems with unobserved interaction pathways.
method An evolution-based approach that characterizes how outcomes change across observation rounds in response to interventions, using an exposure-mapping perspective.
result The approach identifies minimal structural conditions under which evolution mappings exist, enabling consistent learning about heterogeneous spillover effects.
Reduces variance in noisy social outcomes to improve policy evaluation and optimization.
problem Improving access to opportunity through personalized treatment decisions.
method Data-driven dimensionality-reduction using reduced rank regression to denoise multiple outcomes.
result Improves estimation error in policy evaluation and optimization, including on real-world data.
Paper introduces GDR-learners for estimating potential outcomes from observational data.
problem Lack of theoretical property of general Neyman-orthogonality in deep generative models.
method Develops flexible GDR-learners based on various deep generative models.
result GDR-learners possess quasi-oracle efficiency and rate double robustness, asymptotically optimal.
Estimates impulse response functions using machine learning in time series data.
problem Estimating causal effects of discrete treatments over time with flexible models.
method Double/debiased machine learning for nonparametric time series data.
result Consistent and asymptotically normal estimator for impulse response functions.
New method estimates causal effects in complex spaces using topological structures.
problem Challenges in estimating causal effects in non-Euclidean spaces.
method Developed a topological causal inference framework using power-weighted silhouette functions of persistence diagrams.
result Successfully quantifies topological treatment effects across various complex outcomes.
This paper introduces DCE for better counterfactual explanations using optimal transport.
problem Lack of nuanced distributional characteristics in existing counterfactual explanations.
method Formulates a chance-constrained optimization problem using optimal transport to derive counterfactual distributions.
result DCE provides deeper insights into decision-making models by aligning counterfactual distributions with factual ones.
Framework assesses treatment effects by risk groups in observational studies.
problem Evaluating treatment effects in observational studies with risk stratification.
method Five-step framework for risk-based assessment of treatment effect heterogeneity.
result Low-risk patients received negligible absolute benefits, while high-risk patients had pronounced effects.
A successful grasp requires careful balancing of the contact forces. Deducing whether a particular grasp will be successful from indirect measurements, such as vision, is therefore quite challenging, and direct sensing of contacts through touch sensing provides an appealing avenue toward more successful and consistent …
New algorithm optimizes long-term user satisfaction in recommendation systems.
problem Optimizing long-term user satisfaction in recommendation systems with delayed rewards.
method Developed a predictive model of delayed rewards and a bandit algorithm that balances exploration and exploitation.
result Our approach results in substantially better performance compared to short-term or delayed optimization.
We discuss here the mean-field theory for a cellular automata model of meta-learning. The meta-learning is the process of combining outcomes of individual learning procedures in order to determine the final decision with higher accuracy than any single learning method. Our method is constructed from an ensemble of inte…
Method estimates CATE using RCT data to handle hidden confounders.
problem Estimating CATE in the presence of hidden confounders.
method Pseudo-confounder generator and CATE model alignment.
result Method reduces bias in CATE estimation.
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.
The study proves necessary conditions for robust decision-making in uncertain environments.
problem Conditions for robust decision-making in uncertain environments.
method Quantitative selection theorems and binary betting decisions.
result World models, belief-like memory, and persistent variables are necessary for strong task performance.
We consider the problem of sequential learning from categorical observations bounded in [0,1]. We establish an ordering between the Dirichlet posterior over categorical outcomes and a Gaussian posterior under observations with N(0,1) noise. We establish that, conditioned upon identical data with at least two observatio…
New method corrects ML for informative sampling in time-series treatment outcomes.
problem Informative sampling in irregularly observed data hinders accurate treatment outcome forecasting.
method Formalized as covariate shift, proposed inverse intensity-weighting framework, TESAR-CDE.
result TESAR-CDE effectively learns treatment outcomes under informative sampling.
Estimating the long-term effects of treatments is of interest in many fields. A common challenge in estimating such treatment effects is that long-term outcomes are unobserved in the time frame needed to make policy decisions. One approach to overcome this missing data problem is to analyze treatments effects on an int…
Motion analysis is used in computer vision to understand the behaviour of moving objects in sequences of images. Optimising the interpretation of dynamic biological systems requires accurate and precise motion tracking as well as efficient representations of high-dimensional motion trajectories so that these can be use…
The paper targets optimal interventions for long-term outcomes using imputed data and policy learning.
problem Maximizing long-term outcomes observed only in the future.
method Imputing missing long-term outcomes and using a doubly-robust approach for policy evaluation and optimization.
result The approach outperforms simple short-term proxies and achieves significant revenue impact over three years.
Deep Reinforcement Learning (RL) recently emerged as one of the most competitive approaches for learning in sequential decision making problems with fully observable environments, e.g., computer Go. However, very little work has been done in deep RL to handle partially observable environments. We propose a new architec…
Paper learns data-driven organ matching rules from observational data.
problem Tackles organ transplantation compatibility using observational data.
method Representation learning to cluster donors and apply recipient transformations.
result Model outperforms human experts in predicting transplant outcomes.
New theorems show agents need specific internal structures to perform well under uncertainty.
problem How do agents need to be structured to perform well under uncertainty?
method Proved selection theorems showing strong task performance forces specific internal structures.
result Strong task performance forces world models, belief-like memory, and persistent regime-tracking variables.
New framework for estimating treatment effects in observational studies.
problem Estimating average treatment effects in the presence of unobserved confounders.
method Distributionally robust optimization, sensitivity models.
result Sharp bounds on average treatment effects under distributional assumptions.