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We provide adaptive inference methods, based on $\ell_1$ regularization, for regular (semi-parametric) and non-regular (nonparametric) linear functionals of the conditional expectation function. Examples of regular functionals include…

Machine Learning · Statistics 2022-10-25 Victor Chernozhukov , Whitney Newey , Rahul Singh

We propose a double/debiased machine learning framework to estimate average derivative effects in nonparametric panel models with two-way fixed effects. It extends instrumental variable methods to panel settings, handles continuous…

Methodology · Statistics 2026-05-19 Peikai Wu , Kuan Sun , Zhiguo Xiao

This paper combines causal mediation analysis with double machine learning to control for observed confounders in a data-driven way under a selection-on-observables assumption in a high-dimensional setting. We consider the average indirect…

Econometrics · Economics 2021-02-17 Helmut Farbmacher , Martin Huber , Lukáš Lafférs , Henrika Langen , Martin Spindler

Image generative models, particularly diffusion-based models, have surged in popularity due to their remarkable ability to synthesize highly realistic images. However, since these models are data-driven, they inherit biases from the…

Machine Learning · Computer Science 2025-03-18 Lin-Chun Huang , Ching Chieh Tsao , Fang-Yi Su , Jung-Hsien Chiang

This paper focuses on modeling the dynamic attributes of a dynamic network with a fixed number of vertices. These attributes are considered as time series which dependency structure is influenced by the underlying network. They are modeled…

Methodology · Statistics 2019-11-11 Jonas Krampe

High dimensional predictive regressions are useful in wide range of applications. However, the theory is mainly developed assuming that the model is stationary with time invariant parameters. This is at odds with the prevalent evidence for…

Econometrics · Economics 2019-10-09 Kashif Yousuf , Serena Ng

Demystifying effective connectivity among neuronal populations has become the trend to understand the brain mechanisms of Parkinson's disease, schizophrenia, mild traumatic brain injury, and many other unlisted neurological diseases.…

Quantitative Methods · Quantitative Biology 2019-09-27 Po-Ya Hsu

Hierarchical learning models, such as mixture models and Bayesian networks, are widely employed for unsupervised learning tasks, such as clustering analysis. They consist of observable and hidden variables, which represent the given data…

Machine Learning · Statistics 2018-01-08 Keisuke Yamazaki

A fairly reliable trend in deep reinforcement learning is that the performance scales with the number of parameters, provided a complimentary scaling in amount of training data. As the appetite for large models increases, it is imperative…

Machine Learning · Computer Science 2023-06-14 Bogdan Mazoure , Walter Talbott , Miguel Angel Bautista , Devon Hjelm , Alexander Toshev , Josh Susskind

We develop new econometric methods for estimation and inference in high-dimensional panel data models with interactive fixed effects. Our approach can be regarded as a non-trivial extension of the very popular common correlated effects…

Econometrics · Economics 2025-08-11 Maximilian Ruecker , Michael Vogt , Oliver Linton , Christopher Walsh

Longitudinal analysis is important in many disciplines, such as the study of behavioral transitions in social science. Only very recently, feature selection has drawn adequate attention in the context of longitudinal modeling. Standard…

Methodology · Statistics 2016-10-26 Tingyang Xu , Jiangwen Sun , Jinbo Bi

Bias exists in how we pick leaders, who we perceive as being influential, and who we interact with, not only in society, but in organizational contexts. Drawing from leadership emergence and social influence theories, we investigate…

Multiagent Systems · Computer Science 2023-04-06 Andria L. Smith , Simon Heuschkel , Ksenia Keplinger , Charley M. Wu

This study proposes a debiasing method for smooth nonparametric estimators. While machine learning techniques such as random forests and neural networks have demonstrated strong predictive performance, their theoretical properties remain…

Methodology · Statistics 2025-03-19 Masahiro Kato

This work is focussed on the inversion task of inferring the distribution over parameters of interest leading to multiple sets of observations. The potential to solve such distributional inversion problems is driven by increasing…

Machine Learning · Statistics 2026-05-06 Arnaud Vadeboncoeur , Mark Girolami , Andrew M. Stuart

Network modeling techniques provide a means for quantifying social structure in populations of individuals. Data used to define social connectivity are often expensive to collect and based on case-specific, ad hoc criteria. Moreover, in…

We consider estimating a low-dimensional parameter in an estimating equation involving high-dimensional nuisances that depend on the parameter. A central example is the efficient estimating equation for the (local) quantile treatment effect…

Machine Learning · Statistics 2022-08-18 Nathan Kallus , Xiaojie Mao , Masatoshi Uehara

Statistical inference on the explained variation of an outcome by a set of covariates is of particular interest in practice. When the covariates are of moderate to high-dimension and the effects are not sparse, several approaches have been…

Methodology · Statistics 2022-01-24 Hua Yun Chen

Estimating causal effects of continuous treatments is a common problem in practice, for example, in studying average dose-response functions. Classical analyses typically assume that all confounders are fully observed, whereas in real-world…

Statistics Theory · Mathematics 2026-04-14 Shuyuan Chen , Peng Zhang , Yifan Cui

We introduce a double/debiased machine learning estimator for the impulse response function in settings where a time series of interest is subjected to multiple discrete treatments, assigned over time, which can have a causal effect on…

Econometrics · Economics 2025-12-17 Daniele Ballinari , Alexander Wehrli

High-dimensional multivariate longitudinal data, which arise when many outcome variables are measured repeatedly over time, are becoming increasingly common in social, behavioral and health sciences. We propose a latent variable model for…

Methodology · Statistics 2025-12-09 Sze Ming Lee , Yunxiao Chen , Tony Sit
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