The paper proposes a method to find subgroups with significant treatment effects in noisy data.
problem Estimating the causal effects of interventions on noisy outcomes.
method A machine-learning method specifically optimized for finding subgroups with significant effects, designed to maximize the probability of obtaining a statistically significant positive treatment effect.
result The proposed method yields higher power in detecting subgroups affected by the treatment compared to standard tree-based tools.
The paper analyzes how deep neural networks handle noisy labels and finds disparate impacts.
problem Disparate impacts of noisy labels on instances with different representation frequencies.
method Quantifying harms, analyzing solutions, and comparing their impacts on different frequency instances.
result Existing solutions lead to disparate treatments, benefiting higher-frequency instances more.
Develops a method to estimate treatment effects using noisy proxies over time.
problem Estimating individualized treatment effects from noisy proxies of confounders.
method Deconfounding Temporal Autoencoder (DTA) combining autoencoder and causal regularization.
result Improves treatment effect estimates by leveraging noisy proxies and learning hidden confounders.
Study proposes method to estimate causal effects from noisy treatment data.
problem Estimating causal effects from noisy treatment data without side information.
method Deep latent variable model with neural network parameterization and amortized importance-weighted variational objective.
result Causal effect estimates are identifiable without side information and measurement error variance knowledge.
Estimating the individual treatment effect (ITE) from observational data is essential in medicine. A central challenge in estimating the ITE is handling confounders, which are factors that affect both an intervention and its outcome. Most previous work relies on the unconfoundedness assumption, which posits that all th…
Proposes a model to estimate effects of multiple related treatments.
problem Estimating effects of many related treatments in observational data.
method Customized ridge regression to reduce noise and MSE.
result Significantly reduces MSE for individual sub-treatments while allowing reconstruction of aggregated treatment effects.
Develops a method for causal inference with noisy confounders.
problem Noisy measurements of confounders in treatment effects models.
method Local principal subspace approximation combining K-nearest neighbors matching and PCA.
result Estimators of treatment effects and counterfactual distributions are constructed.
Study real-world noisy labels from human annotations for better understanding.
problem Understanding and modeling real-world label noise in machine learning.
method Developed two new benchmark datasets (CIFAR-10N, CIFAR-100N) with human-annotated real-world noisy labels.
result Real-world noisy labels exhibit instance-dependent patterns, not class-dependent as previously assumed.
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.
A novel disentangled graph autoencoder improves treatment effect estimation from networked observational data.
problem Treatment effect estimation from observational data is challenging due to unconfoundedness assumption and latent confounders.
method Proposes a disentangled variational graph autoencoder to disentangle latent factors and enforce factor independence.
result Extensive experiments show superior performance compared to state-of-the-art approaches.
New methods improve estimation accuracy in noisy settings.
problem Estimating treatment effects in the presence of treatment noise.
method Developed new structure-agnostic cumulant estimators and practical procedures for higher-order robustness.
result Demonstrated that existing DML estimator is suboptimal for non-Gaussian treatment noise and introduced ACE procedures for improved accuracy.
Valid causal inference in observational studies often requires controlling for confounders. However, in practice measurements of confounders may be noisy, and can lead to biased estimates of causal effects. We show that we can reduce the bias caused by measurement noise using a large number of noisy measurements of the…
Paper proposes a novel method to assess treatment effect estimators using cross-validation.
problem Lack of ground truth to objectively assess treatment effect estimators in RCTs.
method Cross-validation-like methodology combining noisy difference-of-means estimate and aggregation across RCTs.
result Aggressive downweighting or truncation of large values reduces variance and improves treatment effect estimation.
Bayesian approach for modeling counterfactual distribution and off-policy evaluation.
problem Modeling the counterfactual distribution and off-policy evaluation.
method Bayesian conditional mean embeddings and novel Bayesian methods for estimating ultimate treatment effect.
result Quantifying epistemic uncertainty in the counterfactual distribution and off-policy evaluation.
Method provides statistical guarantees for identifying subgroups in ML studies.
problem Bias and noise in estimating conditional average treatment effects (CATE).
method Develops uniform confidence bands (GATES) for estimating group average treatment effects (GATEs).
result Identifies subgroups with statistical guarantees, regardless of effect size.
Improved IV estimates by weighting on compliance reduces noise in treatment effect estimation.
problem Noisy IV estimates in settings with non-random treatment receipt.
method Weighting observations by estimated compliance, leveraging machine learning for compliance estimation.
result Compliance weighting reduces IV variance, improving precision of treatment effect estimates.
A new method for robust training under label noise using weighted gradient descent.
problem Overfitting to noisy examples in machine learning.
method Exponentiated gradient reweighting for flexible handling of noisy data.
result Improved generalization in noisy classification and PCA problems.
The paper studies how noisy labels impact decision-making in machine learning.
problem The impact of noisy labels on decision-making in machine learning.
method Introducing a notion of regret, studying standard approaches, and estimating individual-level mistakes.
result Standard approaches can lead to unforeseen mistakes for individuals, revealing the need for anticipation.
New framework for choosing optimal proxy metrics from past experiments.
problem Difficult to measure long-term treatment effects in experiments.
method Statistical framework to define and construct optimal proxy metrics.
result Optimal proxy metric depends on experiment's sample size.
Develops a Gaussian-based message-passing algorithm for noisy matrix completion.
problem Noisy matrix completion problems.
method Gaussian parameterized belief propagation algorithm.
result Advantages over traditional methods in non-Gaussian noise scenarios.
Parallel Bayesian optimization tackles noisy multi-objective problems.
problem Optimizing multiple objectives with noisy data.
method NEHVI and qNEHVI acquisition functions, integrating Bayesian treatment over uncertainty. result Parallel qNEHVI is one-step Bayes-optimal and robust to noise. Simulates sepsis treatment decisions using a world model approach.
problem Predicting optimal sepsis treatment actions based on noisy EHR data.
method Uses a Variational Auto-Encoder and Mixture Density Network (MDN-RNN) to model sepsis patient trajectories.
result Simulator learns from MIMIC dataset to predict patient states.
Bayesian method estimates causal effects with proxy networks.
problem Estimating causal effects with only proxy measurements of a latent interference network.
method Structural causal model with Block Gibbs sampler and Locally Informed Proposals.
result Accurately estimates causal effects even with noisy proxy networks.
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.
Variational methods for revealing visual concepts learned by convolutional neural networks have gained significant attention during the last years. Being based on noisy gradients obtained via back-propagation such methods require the application of regularization strategies. We present a mathematical framework unifying…
We present a novel framework for estimation and inference with the broad class of universal approximators. Estimation is based on the decomposition of model predictions into Shapley values. Inference relies on analyzing the bias and variance properties of individual Shapley components. We show that Shapley value estima…
New algorithms for hypothesis testing in high-dimensional data are shown to be effective under various noisy conditions.
problem Testing high-dimensional probability measures under noisy conditions.
method Low coordinate degree functions (LCDF) using Efron-Stein decomposition.
result LCDF can effectively test high-dimensional probability measures under noisy channels, with efficacy depending on scalar Fisher information.
MediEncoder learns nonlinear representations for causal mediation analysis.
problem High-dimensional noisy covariates and mediators in biomedical studies.
method Coupled encoder-decoder architecture with cross-factor network.
result Improves estimation accuracy in high-dimensional causal mediation analysis.
Paper presents a probabilistic diagnostic model for identifying and treating supervised learning degradation issues.
problem Degradation problems in supervised learning, including class imbalance, overlapping, small-disjuncts, noisy labels, and sparseness.
method Develops a novel probabilistic diagnostic model to identify and treat degradation issues in supervised learning.
result Early and correct diagnosis of degradation issues allows for selecting appropriate remediation treatments and unbiased performance metrics.
In a variety of disciplines such as social sciences, psychology, medicine and economics, the recorded data are considered to be noisy measurements of latent variables connected by some causal structure. This corresponds to a family of graphical models known as the structural equation model with latent variables. While …
In a variety of disciplines such as social sciences, psychology, medicine and economics, the recorded data are considered to be noisy measurements of latent variables connected by some causal structure. This corresponds to a family of graphical models known as the structural equation model with latent variables. While …
Study designs logging policies to minimize off-policy evaluation error.
problem Minimizing OPE error with logging policies for target policies.
method Characterizes reward-coverage tradeoff, proposes a unifying framework, derives optimal policies.
result Provides actionable guidance for firms choosing recommendation systems.
Compressed sensing techniques enable efficient acquisition and recovery of sparse, high-dimensional data signals via low-dimensional projections. In this work, we propose Uncertainty Autoencoders, a learning framework for unsupervised representation learning inspired by compressed sensing. We treat the low-dimensional …
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.
M3E2 neural network estimates multiple treatment effects.
problem Estimating effects of multiple treatments simultaneously.
method Multi-task learning neural network model for multiple treatments, continuous and binary.
result M3E2 outperforms baselines in synthetic datasets.
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.
Bayesian neural networks improve cancer dynamics prediction.
problem Predicting cancer dynamics under treatment due to heterogeneity and sparse data.
method Hierarchical Bayesian model using baseline covariates and Bayesian neural networks for nonlinear interactions.
result Bayesian neural networks outperform linear models in predicting cancer dynamics with interactions.
Framework generates personalized insulin treatment strategies using deep models.
problem Developing optimal personalized treatment strategies for diabetes patients.
method Combines deep generative time series models with decision theory.
result Demonstrated improved personalized insulin treatment strategies for diabetes patients.
Develops deep jump learning for continuous treatment OPE.
problem Estimating mean outcomes under new treatment rules using historical data from different rules.
method Adaptive deep discretization of continuous treatment space using deep learning and multi-scale change point detection.
result Validated method through theoretical results, simulations, and real application to Warfarin Dosing.
Method controls treatment risk in learning beneficial allocations.
problem Learning beneficial treatment allocations with risk control in precision medicine.
method Proposes a certifiable learning method that controls treatment risk with finite samples in the partially identified setting.
result Illustrates method using both simulated and real data.
Optimal adaptive experiment for choosing best treatment with binary outcomes.
problem Choosing the best treatment from binary options in an adaptive experiment.
method Adaptive experiment with two phases: treatment allocation and choice. Neyman allocation method used.
result Neyman allocation is minimax and Bayes optimal, matching lower bounds for regret.
We provide a unified treatment of a broad class of noisy structure recovery problems, known as structured normal means problems. In this setting, the goal is to identify, from a finite collection of Gaussian distributions with different means, the distribution that produced some observed data. Recent work has studied s…
Proposes a fusion method for many treatment groups in ITRs.
problem Challenges in handling many treatment groups with data sparsity and covariate imbalance.
method Calibration-weighted treatment fusion procedure that balances covariates and fuses similar treatments.
result Ensures robust treatment group recovery and policy value compared to existing methods.
Dynamic treatment effects estimated over time using covariate balancing.
problem Estimating treatment effects in panel data with dynamic treatments.
method Dynamic covariate balancing with potential local projections.
result Established inferential guarantees for the proposed method.
CRN model estimates treatment effects over time using adversarial balancing.
problem Estimating treatment effects over time in medical settings.
method Adversarial domain balancing to remove time-varying confounders.
result CRN achieves lower error in estimating counterfactuals and treatment timing.
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.
Estimates heterogeneous treatment effects in panel data with a new method.
problem Estimating heterogeneous treatment effects in panel data with general treatment patterns.
method Partition observations into clusters with similar treatment effects using a regression tree, then estimate average treatment effects for each cluster.
result Our method achieves superior accuracy compared to alternative approaches.
In treatment allocation problems the individuals to be treated often arrive sequentially. We study a problem in which the policy maker is not only interested in the expected cumulative welfare but is also concerned about the uncertainty/risk of the treatment outcomes. At the outset, the total number of treatment assign…