This study predicts individual-level events using social dynamics and variational inference.
problem Predicting individual-level events in social systems.
method Stochastic kinetic model and variational inference algorithm.
result The proposed method is more efficient than sampling and achieves high accuracy.
Framework infers coordination strategies from movement data.
problem Inferring individual movement strategies from group data.
method Formalizes Coordination Strategy Inference Problem; provides methodology to infer strategies.
result Framework accurately infers strategies in simulated and real-world datasets.
The paper tackles multi-level fairness in algorithmic systems, addressing bias at both individual and structural levels.
problem Algorithmic systems can unfairly impact marginalized groups, especially when considering only individual-level bias.
method Formalizes multi-level fairness using causal inference tools, addressing effects of sensitive attributes at multiple levels.
result Illustrates the importance of accounting for macro-level sensitive attributes in fairness assessments.
New model reveals voter preferences from aggregate election data.
problem Infer individual-level voter preferences from aggregate election data.
method Modeling aggregate count data as Poisson binomial, relating probabilities to covariates using logistic and neural networks.
result Model predicts voter preferences at precinct and individual levels.
CRISP predicts individual-level COVID-19 risk based on contact data.
problem Estimating individual-level infection risk during the pandemic.
method Probabilistic graphical model using SEIR framework with contact data.
result Model accurately predicts infection spread and recovery times.
The paper addresses modeling individual-level features with aggregated target variables.
problem Modeling individual-level features with aggregated target variables in healthcare databases.
method Generalized linear modeling with a limiting case, exploring permutation testing and alternating imputation.
result The proposed algorithm effectively estimates model parameters and individual level inferences.
Paper presents a framework to infer individual data from aggregate data.
problem Inference of individual-level data from aggregate data due to privacy concerns.
method End-to-end pipeline for processing aggregate data, novel algorithm for reconstruction, machine learning models.
result Valid and usable answers derived from machine learning models using multiple candidate datasets.
Modeling brain connectivity networks with graph-aware inference.
problem Pooling over functional regions loses information and independence assumptions are unreliable.
method Linear mixed effects model accounting for functional regions and edge dependence.
result Interpretable results comparing schizophrenics and healthy controls.
Proposes a new model for estimating individual treatment effects.
problem Estimating individual treatment effects from observational data is challenging.
method Integrates diffusion modeling and conformal inference with propensity score and covariate approximation.
result Establishes rigorous theoretical guarantees and demonstrates competitive performance.
The paper develops methods for causal inference from single-cell RNA sequencing data with multiple outcomes.
problem Causal inference from single-cell RNA sequencing data with multiple heterogeneous outcomes.
method Generic semiparametric inference framework for doubly robust estimation with multiple derived outcomes.
result Demonstrates the use of semiparametric inferential results for estimating causal effects in genomics.
Develops privacy-preserving methods for longitudinal linear regression.
problem Protecting individual information in longitudinal data with privacy-preserving statistics.
method Proposes a user-level private regression estimator and a privatized covariance estimator for longitudinal linear regression under user-level differential privacy.
result Establishes theoretical guarantees for practical user-level differential privacy estimation and inference in longitudinal linear regression.
New model clusters cells and individuals, revealing genetic influences on cell types.
problem Clustering nested data with group-level and observation-level variables.
method Nested Atoms Model (NAM), Bayesian nonparametric approach.
result Identifies clusters of genetically similar individuals with homogeneous cell-type profiles.
Adaptive coverage policies improve conformal prediction accuracy.
problem Fixed coverage levels in traditional conformal prediction lead to uninformative predictions.
method Optimizes adaptive coverage policy using a neural network trained on leave-one-out calibration.
result Adaptive coverage policies produce more informative and flexible prediction sets.
New results for modeling voter probabilities in elections.
problem Modeling voter preferences with aggregate data and individual covariates.
method Maximum likelihood estimation for Poisson binomial distribution, approximated with heteroscedastic Gaussian.
result Existence and curvature results for the MLE of the Poisson binomial likelihood.
SurvCaus improves survival CATE estimation using neural nets.
problem Estimating Individual Treatment Effects (ITE) in survival analysis.
method Representation balancing for counterfactual inference with neural networks.
result The proposed method outperforms baseline methods in synthetic and semisynthetic datasets.
Estimating treatment effects in time series with hidden confounding.
problem Estimating treatment effects in time series with hidden confounding.
method A neural framework that learns individual-level counterfactuals and flexible matching procedures.
result Improves counterfactual estimation under latent bias.
Kernel method optimizes personalized dose rules for patients.
problem Finding optimal individualized dose rules for patients.
method Kernel assisted learning method for estimating optimal dose rules.
result The method identifies the optimal individualized dose rule and produces favorable outcomes.
ClusterSC improves synthetic control by selecting relevant donor groups.
problem The curse of dimensionality in synthetic control with individual-level data.
method ClusterSC incorporates clustering to select relevant donor groups.
result ClusterSC consistently outperforms classical SC approaches.
We present a new approach for transferring knowledge from groups to individuals that comprise them. We evaluate our method in text, by inferring the ratings of individual sentences using full-review ratings. This approach, which combines ideas from transfer learning, deep learning and multi-instance learning, reduces t…
New algorithms estimate individual treatment effects from observational data.
problem Estimating individual treatment effects from observational data.
method Learn a 'balanced' representation to make treated and control distributions similar, using Integral Probability Metrics to measure distribution distances.
result Generalization bounds show expected ITE estimation error is bounded by generalization-error of representation and distribution distance.
Hybrid model learns interpretable meal-level glycemic control.
problem Lack of flexible, interpretable meal-level glycemic control methods.
method Hybrid variational autoencoder grounding latent space to mechanistic differential equation.
result Unsupervised representation discovers separation between individuals based on disease severity.
Unified theory for semiparametric data fusion with individual-level data.
problem Handling data fusion problems, especially in settings with diverse data sources and designs.
method Extending a comprehensive theory to handle conditional and marginal distribution alignments, providing universal results for influence functions and efficient influence functions.
result Paves the way for machine-learning debiased, semiparametric efficient estimation.
Bayesian nonparametric method estimates individualized treatment-response curves from observational data.
problem Estimating individualized treatment-response curves from observational time series data.
method Developed a Bayesian nonparametric method using the G-computation formula.
result BNP method provides more accurate estimates of treatment responses than alternative approaches.
A neural framework corrects bias in estimating individual treatment effects.
problem Estimating individual treatment effects from observational data.
method An anchored neural architecture and precision-corrected intersection-bound inference.
result Corrected bias and maintained nominal coverage in high-dimensional settings.
Develops a method to estimate uncertainty for group-level recommendations in matrix completion.
problem Uncertainty estimation for group-level recommendations in matrix completion.
method Structured conformal inference method combining any matrix completion algorithm.
result Stronger group-level guarantees through structured calibration.
New SDA models for big data analysis using aggregated symbols.
problem Handling large and complex datasets efficiently.
method Developing likelihood functions for symbolic data based on underlying measurement-level data.
result Efficient analysis of big data through reduced distributional summaries.
Bayesian method improves group-level regression uncertainty.
problem Uncertainty in group-level labels.
method Bayesian distribution regression with neural network framework.
result Improved robustness and performance with varying group sizes.
Proposes OpenKI for better web-scale knowledge extraction and alignment.
problem Combining OpenIE and KB for web-scale knowledge extraction and alignment.
method Instance-level inference using neighborhood information from KB and OpenIE extractions, with attention mechanisms.
result Significantly improves performance on OpenIE extractions and semi-structured data.
SD-SCMs generate counterfactual data for causal inference benchmarks.
problem Benchmarking causal inference methods with realistic data.
method Sequence-driven structural causal models (SD-SCMs) for causal inference.
result State-of-the-art methods struggle with individual treatment effect estimation.
New method handles uncertainty in causal effect estimation for better decision-making.
problem Handling uncertainty in causal effect estimation, especially in high-dimensional data and covariate shift.
method Integrates uncertainty estimation into neural network methods for individual-level causal estimates.
result Uncertainty-aware methods improve decision-making by alerting when predictions are not reliable.
New method infers causal effects from noisy confounders using latent variable models.
problem Inferring causal effects from observational data, especially when confounders are unknown or noisy.
method Uses Variational Autoencoders (VAE) to estimate latent space of confounders and causal effect.
result Significantly more robust than existing methods and matches state-of-the-art on benchmarks.
Proposes adversarial learning for counterfactual fairness in machine learning.
problem Ensuring fairness at the individual level by simulating counterfactual samples.
method Adversarial neural learning approach to infer counterfactual samples.
result Significant improvements in counterfactual fairness for both discrete and continuous settings.
Study integrates causal inference and temporal complexity measures to analyze mental health symptoms.
problem Examining how individual symptom trajectories reveal diagnostic patterns in mental disorders.
method Causal inference, graph analysis, temporal complexity measures, machine learning.
result 91% accuracy in diagnosing symptom dynamics, highlighting disorder-specific causal mechanisms.
New models infer causal effects from graph-based time-series data.
problem Inferring causal effects from graph-based relational time-series data.
method Proposes causal inference models leveraging graph topology and time-series data.
result Relational time-series causal inference models accurately estimate local causal effects of individual nodes.
Estimates proxy-based inference adjustments for distribution shifts.
problem Imperfect proxy data leads to biased inference.
method Empirical calibration of proxy-primary metric discrepancy as a random effect.
result Empowers inference without individual-level response data.
ContiVAE estimates individual dose-response curves from unobserved confounders using observational data.
problem Estimating causal effects of continuous treatments considering unobserved confounders.
method Variational auto-encoder with a Tilted Gaussian prior distribution modeling hidden confounders as latent variables.
result ContiVAE outperforms existing methods by up to 62% in predicting individual dose-response curves.
Framework for online resource allocation using social welfare functions.
problem Optimal allocation of resources over time steps in a population.
method Confidence sequence framework for SWF-based online learning and inference, valid for any monotonic, concave, and Lipschitz-continuous SWF.
result Achieves near-optimal regret of i l d e O ( n + n k T ) ilde{O}(n+\sqrt{nkT}) i l d e O ( n + nk T ) for SWF-agnostic algorithm SWF-UCB. Integrates neural encoders into GLMMs for multimodal data analysis.
problem Scalable Bayesian inference for GLMMs assumes low-dimensional tabular predictors and does not handle high-dimensional modalities.
method Jointly learns modality-specific neural encoders with GLMM objective, performs variance-corrected stochastic-gradient MCMC.
result Preserves interpretable fixed and random effects while scaling to large longitudinal datasets.
Reduces risk of model inversion by reducing sensitive feature influence.
problem Model inversion attacks reveal sensitive individual data from trained models.
method Privacy-guided training to reduce sensitive feature influence in tree-based models.
result Training models to reduce sensitive feature influence reduces the risk of inference attacks.
Proposes a novel method to cluster individuals based on treatment effects.
problem Identifying subpopulations with different treatment responses.
method Clusters individuals using a learned kernel derived from causal forests, revealing latent subgroup structures.
result Captures meaningful treatment effect heterogeneity through kernelized clustering.
Model shows how investment traps wealth across generations.
problem Existence of wealth traps between social strata.
method Developed a model linking investment and intergenerational wealth.
result Proved a `rat race' theorem showing investment traps wealth.
New method detects currency contagion sources using causal inference.
problem Lack of causal interpretation in quantifying contagion among currencies.
method Network-based causal inference to identify contagion paths.
result Identifies sources of contagion and diversification options.
Paper proposes a method to estimate individual treatment effects reliably from observational data.
problem Estimating individual treatment effects from observational data is challenging and important.
method The approach uses the Information Bottleneck principle to find more reliable representations for ITE estimation.
result The proposed model achieves state-of-the-art results and provides more reliable prediction performances with uncertainty information.
Two-stage TMLE reduces bias and improves efficiency in CRTs.
problem Differential outcome measurement and imbalance in baseline predictors in CRTs.
method Two-stage targeted minimum loss-based estimator (TMLE) to adjust for baseline covariates.
result Our approach nearly eliminates bias due to differential outcome measurement.
Proposes a deep learning method for modeling dynamic individual-level latent trajectories with changing parameters.
problem Modeling longitudinal data with changing individual-level dynamics parameters.
method Combines deep learning for dimensionality reduction and differential equations for dynamic modeling, allowing different parameters for sub-periods.
result Successfully identifies dynamic parameters and predictors of resilience.
A new method integrates individual and population models for more accurate predictions.
problem How to integrate detailed individual-level models with coarse-grained population-level models.
method Latent Bayesian melding, averaging distributions over population statistics.
result Latent Bayesian melding leads to significantly more accurate predictions than generalized moment matching.
New diagnostics detect variability in individual risk estimates from machine learning models in healthcare.
problem Variability in individual risk estimates from machine learning models in healthcare, leading to unreliable treatment decisions.
method Proposed evaluation framework using empirical prediction interval width and empirical decision flip rate diagnostics.
result Randomness in optimization and initialization can lead to substantial individual-level variability in risk estimates, affecting clinical decisions.
New attack recovers user-level information from large batch images.
problem Recovering private information from user-level gradients in distributed learning.
method Proposes a gradient inversion attack using a denoising diffusion model as a prior.
result Demonstrates recovery of realistic facial images and private attributes.