Investment disputes increase stock volatility, especially for companies with negative outcomes.
problem Investment disputes affect stock market volatility and investor uncertainty.
method Analysis of abnormal share fluctuations and various explanatory variables.
result Investment disputes lead to increased stock volatility, particularly for companies with negative outcomes.
Synapse arbitrates TSFMs to improve time series forecasting performance.
problem TSFMs vary in performance across different forecasting tasks, domains, and horizons.
method Synapse dynamically assigns and adjusts predictive weights based on TSFM performance.
result Synapse consistently outperforms other ensembling techniques and individual TSFMs.
It is well established that humans decision making and instrumental control uses multiple systems, some which use habitual action selection and some which require deliberate planning. Deliberate planning systems use predictions of action-outcomes using an internal model of the agent's environment, while habitual action…
New methods resolve conflicting treatment effect estimates in health tech assessments.
problem Conflicting conclusions from different sponsors analyzing the same data.
method Arbitrated indirect treatment comparisons (ArMAIC) targeting a common target population.
result Estimates treatment effects in a common target population, resolving the MAIC paradox.
This paper improves robot grasping by integrating meta-control and latent-space imagination.
problem Dual-system approaches fail to consider the reliability of the learned model when making multiple-step predictions.
method A meta-controller arbitrates between model-based and model-free decisions based on local reliability, encouraging actions that improve the model and generating imagined experiences for additional training.
result Our approach learns near-optimal grasping policies in dense- and sparse-reward environments, outperforming baseline and state-of-the-art methods.
Computable contracts simplify financial transactions and reduce legal costs.
problem Difficulty in querying, executing, and analyzing text-based financial contracts.
method Develop a Contract Definition Language and illustrate use cases.
result Substantial improvements in customer experience and cost reduction.
Training deep reinforcement learning agents complex behaviors in 3D virtual environments requires significant computational resources. This is especially true in environments with high degrees of aliasing, where many states share nearly identical visual features. Minecraft is an exemplar of such an environment. We hypo…
The paper explores dynamic ensembles for multi-step forecasting.
problem Lack of research on dynamic ensembles for multi-step forecasting.
method Extensive experiments with 3568 time series and an ensemble of 30 multi-output models.
result Dynamic ensembles based on arbitrating and windowing perform best.
Paper simplifies balancing weights by relaxing outcome assumptions.
problem Estimating missing outcomes in a target population.
method Relaxes outcome assumptions to simplify balancing weights.
result Balancing weights can be simplified with convex loss and minimum worst-case bias.
A method corrects bias in estimating a high-dimensional classification rule using auxiliary outcomes.
problem Bias in estimating a high-dimensional classification rule using only one outcome.
method Robust transfer learning approach combining MTL and calibration steps.
result Final estimator achieves lower error than using only the target outcome.
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.
A method to improve time series forecasting by dynamically adjusting weights of forecasters.
problem Challenges in time series forecasting due to evolving data distributions.
method Dynamic re-weighting of forecasters based on evolving data distributions.
result Competitive performance compared to state-of-the-art methods for combining forecasters.
Fuses ITRs for primary and secondary outcomes to minimize harm.
problem Learn an ITR maximizing primary outcome while minimizing harm to secondary outcomes.
method Introduces fusion penalty to encourage similar recommendations for different outcomes. Two algorithms estimate the ITR using surrogate loss functions.
result Agreement rate between primary and secondary optimal ITRs converges faster than ignoring secondary outcomes.
Firms delay write-downs for adverse macroeconomic and industry outcomes but not for firm-specific issues.
problem Timeliness of write-downs for adverse macroeconomic and industry outcomes versus firm-specific issues.
method Comparative analysis of write-downs driven by macroeconomic and industry outcomes versus firm-specific outcomes.
result Firms delay write-downs for adverse macroeconomic and industry outcomes but not for firm-specific issues.
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.
The paper introduces metrics to rank potential outcomes for better decision-making.
problem Optimal action selection in uncertain situations using causal reasoning.
method Introducing two new metrics: probabilities of potential outcome ranking (PoR) and probability of achieving the best potential outcome (PoB). Establishing identification theorems and deriving bounds for these metrics, and presenting estimation methods.
result The estimators' finite-sample properties and their application to a real-world dataset are demonstrated.
Proposes a deep learning framework for estimating counterfactual outcomes.
problem Challenges in estimating individual outcomes under different treatments.
method Deep variational Bayesian framework integrating factual and similar subjects' outcomes.
result Rigorously integrates individual features and similar subjects' responses for counterfactual outcomes.
The study uses transfer learning to compare surgical outcomes across racial/ethnic subgroups.
problem Difficulty in comparing surgical outcomes due to racial/ethnic and geographic differences.
method Causal inference framework and transfer learning to incorporate data from multiple populations.
result Racial and ethnic differences in surgical outcomes are found, with non-Hispanic Black patients experiencing wide variability.
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.
New approach tackles decision-making under predictions that shape outcomes.
problem Challenges in learning optimal decision rules when predictions influence outcomes.
method Introduces performative omniprediction, a predictor that encodes optimal decision rules for multiple objectives.
result Efficient performative omnipredictors exist under a natural restriction of outcome performativity.
New method identifies proxies for causal effects on multiple outcomes.
problem Estimating causal effects in scenarios with multiple outcomes and treatments.
method Causal discovery method leveraging multiple outcomes as proxies for each treatment effect.
result Parallel studies of multiple outcomes can assist in causal identification.
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.
Bayesian optimization learns DM preferences for multi-outcome experiments.
problem Optimizing expensive experiments with unknown utility functions and multiple outcomes.
method Alternates preference learning and Bayesian optimization, using pairwise comparisons.
result Preference exploration strategies improve Bayesian optimization performance.
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.
There is tremendous interest in precision medicine as a means to improve patient outcomes by tailoring treatment to individual characteristics. An individualized treatment rule formalizes precision medicine as a map from patient information to a recommended treatment. A treatment rule is defined to be optimal if it max…
Discusses handling intercurrent events in clinical trials with time-to-event outcomes.
problem Handling intercurrent events in clinical trials with time-to-event outcomes.
method Defines estimands and six ICE handling strategies, including new competing-risk strategy.
result Novel methods for handling intercurrent events in clinical trials with time-to-event outcomes.
We study notions of fairness in decision-making systems when individuals have diverse preferences over the possible outcomes of the decisions. Our starting point is the seminal work of Dwork et al. which introduced a notion of individual fairness (IF): given a task-specific similarity metric, every pair of individuals …
Flow IV uses IVs to infer counterfactuals in complex models.
problem Identifying causal effects and counterfactual reasoning in nonseparable outcome models.
method Utilizes instrumental variables and normalizing flows to estimate and infer counterfactual outcomes.
result Identifies a method to make causal inferences from observed data in nonseparable models.
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.
Two new estimators reduce costs and improve accuracy for EHR outcome prediction.
problem Sparse estimate distributions, high computational cost, and high sampling variance in EHR outcome prediction.
method Proposed SCOPE and REACH estimators that leverage next-token probability distributions.
result SCOPE and REACH match Monte Carlo accuracy with token reductions of 2.5-3.4 times and variance guarantees.
This paper tackles negative transfer in multi-task learning by introducing class-wise weights.
problem Negative transfer hampers function from achieving optimality in multi-task learning.
method Introduces class-wise weights to drive positive transfer and suppress negative transfer.
result Demonstrates improved performance in multi-task learning by reducing negative transfer.
New method combines multiple data sources for optimal decision-making with limited outcomes.
problem Optimal decision-making with limited outcome data from multiple heterogeneous sources.
method Calibrated optimal decision-making method leveraging common intermediate outcomes.
result Proposed estimator of conditional mean outcome is asymptotically normal and more efficient.
Paper proposes a method to estimate counterfactual outcomes without a known SCM.
problem Estimating counterfactual outcomes without a known structural causal model.
method Introduces rank preservation assumption and a novel ideal loss for unbiased learning of counterfactual outcomes.
result The proposed method is effective and unbiased, as shown by theoretical analysis and experiments.
A new method removes biases in data integration by using surrogate control outcomes.
problem Data integration methods can be biased due to data-dependent processes.
method Post-integrated inference method using surrogate control outcomes to account for latent heterogeneity.
result The method provides consistent and efficient estimators under minimal assumptions and potential misspecifications.
Unified approach for estimating quantiles of potential outcomes using inverse estimating equations.
problem Estimating quantiles of potential outcomes for causal inference.
method Inverse estimating equations and moment function.
result Unified approach to estimate mean and quantiles of potential outcomes.
Achilles Tendon Rupture (ATR) is one of the typical soft tissue injuries. Rehabilitation after such a musculoskeletal injury remains a prolonged process with a very variable outcome. Accurately predicting rehabilitation outcome is crucial for treatment decision support. However, it is challenging to train an automatic …
This paper proposes a method for estimating the effect of a policy intervention on an outcome over time. We train recurrent neural networks (RNNs) on the history of control unit outcomes to learn a useful representation for predicting future outcomes. The learned representation of control units is then applied to the t…
Caus-Modens uses deep ensembles to better predict causal outcomes in hidden confounding scenarios.
problem Predicting causal outcomes in the presence of hidden confounders.
method Caus-Modens employs a modulated ensemble approach to improve prediction intervals for causal outcomes using sensitivity models.
result Caus-Modens provides tighter prediction intervals for causal outcomes compared to existing methods.
MLHO predicts COVID-19 adverse outcomes using past medical records.
problem Predicting adverse outcomes after COVID-19 infection.
method Iterative feature and algorithm selection, sequential representation mining.
result Mean AUC ROC of 0.91 for mortality prediction.
Develops a Causal Transformer for estimating counterfactual outcomes from longitudinal data.
problem Estimating counterfactual outcomes over time from observational data is challenging due to complex, long-range dependencies.
method Combines three transformer subnetworks with separate inputs for time-varying covariates, previous treatments, and previous outcomes into a joint network with in-between cross-attentions. Uses a custom, end-to-end training procedure with a counterfactual domain confusion loss to address confounding bias.
result Achieves superior performance over current baselines in synthetic and real-world datasets.
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.
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.
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.
Develops new methods to estimate treatment effects in survival data with competing risks.
problem Estimating treatment effects in survival data with competing risks.
method Censoring Unbiased Transformations (CUTs) for survival outcomes with and without competing risks.
result Consistent estimates of heterogeneous cumulative incidence effects and total effects using HTE learners.
Improved AutoDML estimator for causal inference using outcome-adapted shared covariate representation.
problem Efficiency in estimating treatment or policy effects in causal inference.
method Outcome-adapted AutoDML estimator that uses a shared covariate representation that is predictive of the outcome but not the Riesz representer.
result Outcome-adapted AutoDML estimator is asymptotically more efficient than baseline AutoDML.
Deep neural networks for ordinal outcomes combining image and tabular data.
problem Lack of interpretable models for ordinal outcomes in mixed data types.
method Ordinal Neural Network Transformation Models (ONTRAMs) integrating DL and classical ordinal regression.
result ONTRAMs achieve performance equivalent to standard multi-class DL models but are faster and more interpretable.
New method uses AI predictions as cheaper alternatives to expensive outcomes.
problem Using expensive outcomes for statistical inference.
method Recalibrated prediction-powered inference using machine learning techniques.
result Significant gains in effective sample size over existing PPI proposals.
New decompositions misattribute differences between populations, even when outcomes are identical.
problem Misattribution of differences between populations using common functional decompositions.
method Extending the Kitagawa-Oaxaca-Blinder decomposition to nonlinear functional decompositions.
result Functional ANOVA and Accumulated Local Effects can misattribute differences even when outcomes are identical in two populations.