Bayesian method estimates QTEs from observational data.
problem Estimating nuanced characteristics of counterfactual distributions.
method Bayesian semiparametric conditional distribution regression model with double balancing score.
result Proposed method provides more accurate QTE estimates than other methods.
Proposes a scalable method for counterfactual prediction using machine learning.
problem De-bias causal estimators with high-dimensional data in observational studies.
method Uses entropy balancing to learn weights minimizing Jensen-Shannon divergence, leading to robust counterfactual predictions.
result Consistent causal estimation if either propensity score or outcome model is correctly specified.
The paper argues for using Neyman orthogonal score for balancing in debiased machine learning.
problem Debiased machine learning requires a proper approach to balance covariates.
method The paper advocates for using Riesz regression with basis functions of X for balancing.
result Covariate balancing is only valid when the score-relevant regression error is a function of covariates alone.
We construct a generalization of twistor spaces of hypercomplex manifolds and hyper-Kahler manifolds M M M , by generalizing the twistor P 1 \mathbb{P}^{1} P 1 to a more general complex manifold Q Q Q . The resulting manifold X X X is complex if and only if Q Q Q admits a holomorphic map to P 1 \mathbb{P}^1 P 1 . We make branched double cove…
The paper investigates how calibrating propensity scores improves DML estimates of average treatment effects.
problem Improving the accuracy of DML estimates in finite samples.
method Propensity score calibration within the Double/debiased machine learning framework.
result Calibrating propensity scores reduces the root mean squared error of DML estimates of average treatment effects in finite samples.
Neural score matching improves high-dimensional causal inference by using neural networks for balancing scores.
problem Impracticality of traditional matching methods in high-dimensional datasets due to the curse of dimensionality.
method Develops neural networks to create non-trivial, multivariate balancing scores for high-dimensional causal inference.
result Neural score matching outperforms other methods in treatment effect estimation and reducing imbalance on high-dimensional datasets.
Novel characterization of augmented balancing weights combining outcome and weighting models.
problem Improving estimation accuracy in machine learning models with balancing weights.
method Characterization of augmented balancing weights as linear models, extending to ridge and lasso regression.
result Equivalence and closed-form expressions for specific model choices, providing insights into performance.
Proposes a novel neural network method to estimate average treatment effect.
problem Bias in estimating average treatment effect due to confounding and instrumental variables.
method Self-balancing neural network (Sbnet) that estimates pseudo propensity scores and average treatment effect in one step.
result Proposed method outperforms state-of-the-art methods in simulations and real-world datasets.
The paper constructs flat metrics on orbifolds and resolutions.
problem Finding flat metrics on orbifolds and their resolutions.
method Gluing construction and analysis of singularities.
result All crepant resolutions of non-Kähler Calabi-Yau orbifolds with Chern-Ricci flat balanced metrics admit such metrics.
Paper introduces new estimator for continuous treatment effects.
problem Estimating the average dose-response function of continuous treatments.
method Utilizes ADML and DML tools, with a novel debiasing method.
result Proves asymptotic normality and shows good performance in simulations.
Improves robustness of propensity score estimators in challenging settings.
problem Limited overlap, small sample sizes, or unbalanced data.
method Extends calibration techniques for propensity score models, focusing on sample-splitting schemes.
result Calibration reduces variance and bias in inverse probability weighting and double/debiased machine learning frameworks.
Bayesian design improves by reducing policy training cost.
problem Double intractability in expected information gain limits policy learning.
method Score matching to isolate EIG, then train policy singly intractably.
result Reduced computational burden for policy training, allowing multiple iterations.
Reinforcement learning studies how to balance exploration and exploitation in real-world systems, optimizing interactions with the world while simultaneously learning how the world operates. One general class of algorithms for such learning is the multi-armed bandit setting. Randomized probability matching, based upon …
Paper proposes a new method for estimating treatment effects using interpretable deep learning models.
problem Estimating treatment effects from observational data with interpretability.
method Proposes a novel objective function using energy distance balancing score and neural additive models for improved interpretability.
result Demonstrates superior performance over state-of-the-art methods in semi-synthetic experiments.
The paper proposes a new method for covariate balancing using IPM to improve causal inference.
problem Covariate imbalance in causal inference weighting methods, especially when models are not correctly specified.
method The integral probability metric (IPM) is used to determine optimal weights for treated and control groups.
result The proposed method can be consistent without specifying either the propensity score or outcome regression model.
New examples of Schoenflies balls are produced using a 5D approach.
problem Identifying Schoenflies balls that are not standard.
method Using a 5-dimensional perspective, algebraic and geometric handle cancellation.
result New examples of Schoenflies balls not known to be standard are produced.
This paper explains double descent using VC theory.
problem Understanding the generalization of overfitting neural networks.
method VC-theoretical analysis of double descent.
result Double descent can be explained by classical VC-generalization bounds.
This work improves structured prediction by learning the balance between signal and random noise.
problem Structured prediction with random perturbations.
method Learning the variance of randomized structured predictors to balance signal and noise.
result Learning the balance improves structured prediction effectiveness.
A new algorithm improves credit scoring accuracy for imbalanced data.
problem Poor classification of minority class in credit scoring data sets.
method Weighted-Hybrid-Sampling-Boost (WHSBoost) algorithm with balanced data sampling.
result WHSBoost outperforms other methods in credit scoring accuracy.
The paper uses double machine learning to estimate dynamic treatment effects robustly.
problem Estimating causal effects of dynamic treatments with time-varying covariates.
method Double machine learning with Neyman-orthogonal score functions for robustness.
result Asymptotic normality and n \sqrt{n} n -consistency of the estimators under specific conditions. The Synthetic Minority Oversampling TEchnique (SMOTE) is widely-used for the analysis of imbalanced datasets. It is known that SMOTE frequently over-generalizes the minority class, leading to misclassifications for the majority class, and effecting the overall balance of the model. In this article, we present an approa…
Several structure learning algorithms have been proposed towards discovering causal or Bayesian Network (BN) graphs. The validity of these algorithms tends to be evaluated by assessing the relationship between the learnt and the ground truth graph. However, there is no agreed scoring metric to determine this relationsh…
Balance corrects biased survey data for more accurate insights.
problem Bias in survey data leads to inaccurate insights and underperforming models.
method Three steps: bias understanding, weight adjustment, and evaluation.
result Corrected data leads to more accurate ML model training and insights.
Proposes a method to balance fairness and utility in ranking models.
problem Systematic disparity across protected groups in ranking models.
method Model-agnostic post-processing framework using dynamic programming.
result Achieves a balance between fairness and utility across various metrics and datasets.
We prove the double bubble conjecture in the three-sphere S 3 S^3 S 3 and hyperbolic three-space H 3 H^3 H 3 in the cases where we can apply Hutchings theory: 1) in S 3 S^3 S 3 , each enclosed volume and the complement occupy at least 10% of the volume of S 3 S^3 S 3 ; 2) in H 3 H^3 H 3 , the smaller volume is at least 85% that of the larger. A balanc…
New algorithm improves causal effect estimation for continuous treatments.
problem Observational causal inference with continuous treatments.
method End-to-end entropy balancing for maximizing causal inference accuracy.
result Our algorithm estimates causal effect more accurately than baseline.
In this paper, we propose a robust method to estimate the average treatment effects in observational studies when the number of potential confounders is possibly much greater than the sample size. We first use a class of penalized M-estimators for the propensity score and outcome models. We then calibrate the initial e…
We used convolutional neural networks (CNNs) for automatic sleep stage scoring based on single-channel electroencephalography (EEG) to learn task-specific filters for classification without using prior domain knowledge. We used an openly available dataset from 20 healthy young adults for evaluation and applied 20-fold …
FairPOT balances fairness and AUC performance by selectively transforming risk scores.
problem Balancing fairness and AUC performance in high-stakes domains.
method FairPOT uses proportional optimal transport to selectively transform risk scores.
result FairPOT consistently improves fairness with minimal AUC degradation or even positive gains.
Proposes a method to stabilize treatment effect estimation with unbalanced data.
problem Unbalanced treatment assignment leading to unstable propensity score estimations.
method Undersamples data for propensity score modeling and calibrates scores to match original distribution.
result The estimator retains asymptotic properties of the DML estimator and improves finite sample performance.
Diffusion models don't overfit, contrary to expectations.
problem Understanding generalization in diffusion models.
method Fundamental impossibility results and analysis of score matching.
result Diffusion models exhibit classical U-shaped loss curve, not double descent.
SJDs unify masked, continuous, and hybrid diffusion models.
problem Unified modeling of diffusion processes.
method Continuous-time Markov processes with token embeddings and hazard rates.
result Unified model recovers masked, continuous, and hybrid diffusion as limits.
Synthetic data augmentation can improve imbalanced classification metrics.
problem Improving imbalanced classification metrics
method Developing a framework for analyzing the effects of synthetic data augmentation on score-based classification
result Augmentation can improve AUROC, AUPRC, balanced accuracy, and F1 score
Observational cohort studies with oversampled exposed subjects are typically implemented to understand the causal effect of a rare exposure. Because the distribution of exposed subjects in the sample differs from the source population, estimation of a propensity score function (i.e., probability of exposure given basel…
Post-calibration improves the accuracy of causal effect estimation.
problem Improperly calibrated propensity scores lead to inaccurate causal effect estimation.
method Performed a simulation study to assess the impact of post-calibration on causal effect estimation.
result Post-calibration reduces the error in estimating the average treatment effect, especially for expressive uncalibrated statistical estimators.
The paper explores fair machine learning policies for balancing competing objectives in noisy data.
problem Balancing competing objectives in noisy data.
method Analyzes a class of policies that trace an empirical Pareto frontier based on learned scores.
result Characterizes optimal strategies and bounds Pareto errors due to score inaccuracies.
Study examines fairness in machine learning for credit scoring.
problem Bias in machine learning models for credit scoring.
method Comprehensive experimental study of fairness-aware machine learning models.
result Fairness-aware models improve fairness while maintaining accuracy.
Periodic Double Auctions (PDAs) are commonly used in the real world for trading, e.g. in stock markets to determine stock opening prices, and energy markets to trade energy in order to balance net demand in smart grids, involving trillions of dollars in the process. A bidder, participating in such PDAs, has to plan for…
Proposes RCL method to improve ATE estimation from observational data.
problem Error-compounding issue and extreme estimates in DML estimators.
method Robust Causal Learning (RCL) method to offset DML deficiencies.
result RCL estimators are more stable and perform better than DML estimators.
Estimates system parameters from a single observation using kernel-based score.
problem Estimating parameters of a dynamical system from a high-dimensional signal.
method Kernel-based score to compare temporal dependencies between signal and model.
result Accuracy and efficiency demonstrated on chaotic systems.
Proposes methods to estimate posterior probability and propensity score functions without assuming constant propensity score.
problem Learning from biased positive-unlabeled data.
method Parametric approach to joint estimation of posterior probability and propensity score functions using maximum likelihood and alternating maximization.
result Proposed methods are comparable or better than existing methods based on Expectation-Maximisation scheme.
Chernozhukov, Chetverikov, Demirer, Duflo, Hansen, and Newey (2016) provide a generic double/de-biased machine learning (DML) approach for obtaining valid inferential statements about focal parameters, using Neyman-orthogonal scores and cross-fitting, in settings where nuisance parameters are estimated using a new gene…
MAFLA improves sampling from heavy-tailed distributions using MH-inspired corrections.
problem Sampling from heavy-tailed and multimodal distributions when neither target nor proposal densities can be evaluated.
method Metropolis-Adjusted Fractional Langevin Algorithm (MAFLA) with Score Balance Matching.
result MAFLA significantly improves finite-time sampling accuracy over unadjusted fractional Langevin dynamics.
FIDDLE uses deep learning to estimate ATE from complex data.
problem Estimating ATE from high-dimensional, correlated covariates with sparse nonlinear effects.
method Factor-augmented deep learning for propensity and outcome models.
result FIDDLE consistently estimates ATE under model misspecification and is semiparametrically efficient.
Improves generative models by using heavy-tailed noise in score matching.
problem High-dimensional limitations of Gaussian noise in generative models.
method Extended DSM to generalised normal distribution, relaxed key assumptions, developed iterative noise scaling algorithm.
result Heavy-tailed DSM leads to improved generative performance.
Study finds flipped classrooms improve student self-concept, enjoyment, but not exam scores.
problem Evaluating the impact of flipped classrooms on higher education outcomes.
method Double/debiased machine learning (DML) approach to analyze student data.
result No significant positive effects on exam scores, passing rates, or knowledge retention.
Paper proposes ADC framework to reduce ViT SL training communication overhead.
problem Reducing communication overhead in ViT SL training.
method Two parallel compression strategies: class-agnostic merging and token discarding.
result Significantly reduces communication overhead without sacrificing accuracy.
C- t 3 t^3 t 3 VAE improves class representation in long-tailed generative models.
problem Latent geometric bias in VAEs under class imbalance.
method Per-class Student's t-distribution priors, closed-form objective, equal-weight latent mixture.
result Consistently lower FID scores and better class-balanced generation for severely imbalanced datasets.