The paper addresses misspecification in econometric models of discrete unobserved heterogeneity.
problem Misspecification in econometric models of discrete unobserved heterogeneity.
method Generalizing previous approaches to allow multiple latent variables, developing inference results for a k-means style estimator, and proposing information criteria for model selection.
result Over-fitting can be severe in k-means style estimators when the number of clusters is over-specified.
Paper adapts DML for panel data, addressing unobserved heterogeneity.
problem Estimating causal effects with panel data and unobserved heterogeneity.
method Adapting double/debiased machine learning (DML) for panel data with predictive models based on correlated random effects.
result Predictive models based on correlated random effects within DML lead to accurate coefficient estimates.
New method removes hidden confounders for unbiased treatment effect estimation.
problem Bias in treatment effect estimation due to unobserved confounders.
method Proposes a new debiased estimation approach via SVD to handle heterogeneous confounding.
result Established rate of convergence for the estimator under different noise conditions.
Kernel methods identify treatment effects with unobserved confounding using negative controls.
problem Learning causal relationships with unmeasured confounding.
method Kernel ridge regression algorithms for nonparametric treatment effects.
result Uniform consistency and finite sample rates of convergence proved.
We develop a cross-sectional research design to identify causal effects in the presence of unobservable heterogeneity without instruments. When units are dense in physical space, it may be sufficient to regress the "spatial first differences" (SFD) of the outcome on the treatment and omit all covariates. The identifyin…
This paper examines a heterogeneous beliefs model in which there is a process that is only partially observed by the agents. The economy contains a risky asset producing dividends continuously in time. The dividends are observed by the agents. The dividends are assumed to be a known function of some other unobserved pr…
KRCD detects unobserved confounders in nonlinear observational data.
problem Detecting unobserved confounders in nonlinear observational studies.
method Kernel Regression Confounder Detection (KRCD) using reproducing kernel Hilbert spaces.
result KRCD outperforms existing methods and achieves superior computational efficiency.
Combines IV and observational data to estimate CATEs with low compliance and unobserved confounding.
problem Estimating CATEs in personalized medicine and analytics with observational data and weak IVs.
method Two-stage framework: first learns biased CATEs from observational data, then corrects using IV data.
result Effective in estimating CATEs with low compliance and unobserved confounding.
Dyadic Data Prediction (DDP) is an important problem in many research areas. This paper develops a novel fully Bayesian nonparametric framework which integrates two popular and complementary approaches, discrete mixed membership modeling and continuous latent factor modeling into a unified Heterogeneous Matrix Factoriz…
Logit-link models reveal socio-temporal effects on microfinance delinquency.
problem Understanding and quantifying socio-temporal factors affecting microfinance loan delinquency.
method Developed and evaluated discrete-time logit-link models with fixed-effects and frailty extensions.
result Simple random intercept structures capture latent heterogeneity in microfinance repayment behavior.
CLOUD method detects causal relationships in various data types without latent variable assumptions.
problem Detecting causal relationships in the presence of unobserved common causes.
method CLOUD method using Normalized Maximum Likelihood (NML) Code for various data types (discrete, mixed, continuous).
result CLOUD method is more effective than existing methods in inferring causal relationships.
CDVAE estimates treatment effects over time by accounting for unobserved variables.
problem Estimating treatment effects over time in the presence of unobserved confounders.
method Causal Dynamic Variational Autoencoder (CDVAE) that addresses unconfoundedness and unobserved heterogeneity.
result CDVAE outperforms existing methods in estimating Conditional Average Treatment Effects (CATEs).
VSAE learns from missing heterogeneous data by modeling latent dependencies.
problem Learning from partially-observed heterogeneous data with missingness.
method Variational selective autoencoder (VSAE) models joint distribution of observed, unobserved, and missing data.
result VSAE improves over state-of-the-art models in data generation and imputation tasks.
In online social networks people often express attitudes towards others, which forms massive sentiment links among users. Predicting the sign of sentiment links is a fundamental task in many areas such as personal advertising and public opinion analysis. Previous works mainly focus on textual sentiment classification, …
New method identifies latent treatment effects from proxy models.
problem Identifying heterogeneous treatment effects under unobserved confounding.
method Compressed observable operator and spectral analysis of treatment effects.
result Eigenvalues of the operator represent latent treatment effects.
Framework tests CATE homogeneity across trials and evaluates confounding.
problem Assessing treatment effect consistency across randomized and observational studies.
method Leverages multiple randomized trials to test CATE homogeneity and compares with observational data.
result Identifies potential confounding and effect heterogeneity in treatment effects.
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.
SHIFT method optimally estimates heterogeneous discrete distributions with limited communication.
problem Collaborative learning of discrete distributions under heterogeneity and communication constraints.
method Two-stage method: First, users learn a central distribution; then, fine-tune this to estimate individual distributions.
result SHIFT is minimax optimal in the model of heterogeneity and under communication constraints.
This study analyzes decision-making in diverse environments where past data may not predict future outcomes.
problem How to make decisions when past data is not indicative of future outcomes due to unobserved confounders.
method Developed a framework to analyze and bound the performance of data-driven policies in heterogeneous environments.
result Established a method to upper bound the asymptotic worst-case regret of policies and analyzed the performance of Sample Average Approximation (SAA).
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.
The paper introduces a model to measure ASR fairness, addressing key issues.
problem Measuring fairness in ASR systems for different subgroups.
method Mixed-effects Poisson regression to control nuisance factors and handle unobserved heterogeneity.
result The method effectively addresses WER gaps among subgroups and is flexible for practical analyses.
Method learns causal effects from multiple interventions in presence of unobserved confounders.
problem Disentangling causal effects from sets of interventions in the presence of unobserved confounders.
method Non-linear structural causal models with additive, multivariate Gaussian noise; algorithm that learns causal model parameters by pooling data from different regimes and maximizing combined likelihood.
result Identification proofs demonstrate that causal effects of single interventions can be learned from sets of interventions, even with unobserved confounders.
New method tackles OOD robustness with a single additional variable.
problem Out-of-distribution generalization with unobserved confounders.
method Identifiability assumptions using a single additional variable.
result Superior empirical performance on benchmark tasks.
Study on identifying and inferring nonlinear dynamics on unknown networks.
problem Identifying network structure in nonlinear dynamic systems with unknown interactions.
method Showed network structure is not generically identified, requiring sufficient spectral heterogeneity. Developed necessary and sufficient conditions for identification and proposed a semiparametric estimator.
result Necessary and sufficient conditions for identification of network structure in nonlinear dynamic systems.
Many scientific and engineering challenges -- ranging from pharmacokinetic drug dosage allocation and personalized medicine to marketing mix (4Ps) recommendations -- require an understanding of the unobserved heterogeneity in order to develop the best decision making-processes. In this paper, we develop a hypothesis te…
Single proxy variable helps estimate causal effects from confounders.
problem Estimating causal effects from treatment to outcome when unobserved confounders are present.
method Assumes a single, potentially multi-dimensional proxy variable of the unobserved confounder and a known mechanism generating the proxy from the confounder. Proves causal effects are identifiable under completeness assumption.
result Causal effects are identifiable under SPICE assumption.
MDCN improves treatment effect estimation in multicenter observational studies.
problem Incongruities in multicenter observational studies due to center-specific protocols and treatment reactions.
method MDCN learns a new feature embedding to address selection bias and strengthen information sharing between similar centers.
result MDCN provides more accurate treatment insights for new, unobserved centers compared to existing methods.
New method selects best HTE estimator without ground-truth treatment effects.
problem Selecting best HTE estimator from multiple candidates.
method Cross-fitted, exponentially weighted test statistic with two-way sample splitting.
result Empirically, reliable error control and reduced false selections.
New framework tackles stochastic latent subgroup heterogeneity in online decision-making.
problem Stochastic latent heterogeneity in online decision-making where individual responses vary with unobserved subgroups.
method Latent heterogeneous bandit framework using EM-greedy algorithm to learn subgroup probabilities and reward parameters.
result Achieves optimal estimation and classification guarantees, revealing a fundamental stochastic barrier in online decision-making.
A new method measures heterogeneity without needing categorical partitioning or distance measurement.
problem Measuring heterogeneity in non-categorical data requires categorical partitioning and distance measurement, limiting applicability.
method Representational Rényi heterogeneity (RRH) transforms data into a latent space where heterogeneity can be measured without these requirements.
result RRH can generalize existing indices and better responds to changes in mixture component separation and weighting.
Method improves robustness and generalizability of CATE estimation.
problem Lack of external validity in site-specific models for diverse populations.
method Minimax-regret framework with robust optimization.
result Interpretable closed-form solution for generalizable CATE model.
This article analyzes the problem of estimating the time until an event occurs, also known as survival modeling. We observe through substantial experiments on large real-world datasets and use-cases that populations are largely heterogeneous. Sub-populations have different mean and variance in their survival rates requ…
A method for inferring motility models and heterogeneity from particle trajectories.
problem Understanding motility patterns from discrete trajectory data of biological agents.
method Maximum likelihood approach for second-order Langevin models with population heterogeneity.
result The proposed method outperforms alternative approaches for short trajectories.
Regime-switching models, in particular Hidden Markov Models (HMMs) where the switching is driven by an unobservable Markov chain, are widely-used in financial applications, due to their tractability and good econometric properties. In this work we consider HMMs in continuous time with both constant and switching volati…
A matrix completion problem, which aims to recover a complete matrix from its partial observations, is one of the important problems in the machine learning field and has been studied actively. However, there is a discrepancy between the mainstream problem setting, which assumes continuous-valued observations, and some…
We estimate prices of exotic options in a discrete-time model-free setting when the trader has access to market prices of a rich enough class of exotic and vanilla options. This is achieved by estimating an unobservable quantity called "implied expected signature" from such market prices, which are used to price other …
CADD improves generative quality by augmenting discrete diffusion with continuous latent space.
problem Loss of semantic information between denoising steps in discrete diffusion models.
method Introduces a framework that augments discrete state space with a continuous latent space, allowing for graded, informative masked tokens.
result CADD improves generative quality across text generation, image synthesis, and code modeling.
Paper introduces Functional Effects Models to account for individual heterogeneity in panel data.
problem Accounting for preference heterogeneity in panel data with machine learning.
method Functional Effects Models using gradient boosting decision trees and deep neural networks to learn individual-specific preference parameters.
result Functional Effects Models outperform traditional models in learning inter-individual heterogeneity and predictive performance.
Individuals do not respond uniformly to treatments, events, or interventions. Sociologists routinely partition samples into subgroups to explore how the effects of treatments vary by covariates like race, gender, and socioeconomic status. In so doing, analysts determine the key subpopulations based on theoretical prior…
Study Nash equilibrium in market with relative wealth concerns under partial information and heterogeneous priors.
problem Analyzing Nash equilibrium in a market with unobservable return rates and heterogeneous priors.
method Established a Nash equilibrium through a separation result and martingale argument. Used fully-coupled linear FBSDEs and deep neural networks for numerical computation.
result Investment strategies under relative wealth concerns exhibit a herd effect, with accurate prior estimators leading the market.
Optimal insurance contracts are designed to screen risk preferences and risk types under asymmetric information.
problem Designing optimal insurance contracts under asymmetric information and risk types.
method Constructing a menu of contracts that maximizes mean-variance utilities, subject to truth-telling constraints.
result Equilibrium contracts exhibit nonlinear pricing with decreasing risk loadings, inducing self-selection.
The paper explains stock market predictability through a model of heterogeneous beliefs.
problem Understanding and predicting stock market behavior based on news and investor beliefs.
method A discrete-time model of heterogeneous beliefs where some agents receive noisy signals about asset fundamentals.
result Momentum and reversal in stock prices arise from investors' incorrect beliefs about signal accuracy and fundamental values.
SurvHTE-Bench benchmarks HTE estimation in survival analysis with diverse datasets.
problem Challenges in estimating HTEs from right-censored survival data.
method Modular synthetic datasets, semi-synthetic datasets, and real-world datasets.
result First rigorous comparison of survival HTE methods under diverse conditions.
Paper identifies unobserved variables from observable data.
problem Missing variables in empirical studies.
method Function mapping from observables to unobservables based on joint distribution.
result Uniqueness of latent values in each observation.
Bayesian approach groups observations with similar effects for better inference.
problem Estimating heterogeneity across observations in political science.
method Structured sparsity framework integrated into Bayesian regression analysis.
result Method outperforms state-of-the-art methods for heterogeneous effects estimation.
Proposes a generalized causal tree for handling multiple treatments in uplift modeling.
problem Handling multiple treatments in uplift modeling.
method Generalizes causal tree algorithm to handle multiple discrete and continuous-valued treatments.
result Demonstrates improved performance over existing methods in experiments and real data examples.
Neural networks' feature geometry evolves like discrete Ricci flow.
problem Understanding neural feature representations and their geometric transformations.
method Approximating input manifold with geometric graphs and analyzing their evolution during training.
result Neural feature geometry evolves like discrete Ricci flow, with nonlinear activations playing a crucial role.
HL-VAE extends VAE for heterogeneous temporal and longitudinal data.
problem Handling heterogeneous data in temporal and longitudinal datasets.
method Proposes HL-VAE, an extension of existing VAEs for temporal and longitudinal data, incorporating likelihood models for various data types.
result HL-VAE achieves competitive performance in missing value imputation and predictive accuracy.