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Found 38 papers, 8 papers with code
Estimation and Inference for Causal Functions with Multiway Clustered Data
This paper proposes methods of estimation and uniform inference for a general class of causal functions, such as the conditional average treatment effects and the continuous treatment effects, under multiway clustering.
Mining Path Association Rules in Large Property Graphs (with Appendix)
In this paper, we introduce the problem of path association rule mining (PARM).
The Informativeness of Combined Experimental and Observational Data under Dynamic Selection
This paper addresses the challenge of estimating the Average Treatment Effect on the Treated Survivors (ATETS; Vikstrom et al., 2018) in the absence of long-term experimental data, utilizing available long-term observational data instead.
Evaluating Fairness Metrics Across Borders from Human Perceptions
Which fairness metrics are appropriately applicable in your contexts?
High-Dimensional Tail Index Regression: with An Application to Text Analyses of Viral Posts in Social Media
To conduct inference, we propose to debias the regularized estimate, and establish the asymptotic normality of the debiased estimator.
A Bracketing Relationship for Long-Term Policy Evaluation with Combined Experimental and Observational Data
Combining short-term experimental data with observational data enables credible long-term policy evaluation.
Efficient and Explainable Graph Neural Architecture Search via Monte-Carlo Tree Search
Furthermore, we show ExGNAS is effective in analyzing the difference between GNN architectures in homophilic and heterophilic graphs.
On the Inconsistency of Cluster-Robust Inference and How Subsampling Can Fix It
Conventional methods of cluster-robust inference are inconsistent in the presence of unignorably large clusters.
A Simple and Scalable Graph Neural Network for Large Directed Graphs
In response, we propose a simple yet holistic classification method A2DUG which leverages all combinations of node representations in directed and undirected graphs.
Ranked #1 on
Node Classification
on wiki
Learned spatial data partitioning
Due to the significant increase in the size of spatial data, it is essential to use distributed parallel processing systems to efficiently analyze spatial data.
Doubly Robust Estimators with Weak Overlap
In this paper, we derive a new class of doubly robust estimators for treatment effect estimands that is also robust against weak covariate overlap.
Scardina: Scalable Join Cardinality Estimation by Multiple Density Estimators
In recent years, machine learning-based cardinality estimation methods are replacing traditional methods.
On Using The Two-Way Cluster-Robust Standard Errors
We, therefore, hope that this paper will provide a theoretical justification for the legitimacy of most, if not all, of the thousands of those empirical papers that have used the TWCR standard errors.
Extreme Changes in Changes
This paper proposes a new CIC estimator to accurately estimate treatment effects at extreme quantiles.
Non-Robustness of the Cluster-Robust Inference: with a Proposal of a New Robust Method
In light of these negative results about the existing CR methods, we propose a weighted CR (WCR) method as a simple fix.
Estimation of Average Derivatives of Latent Regressors: With an Application to Inference on Buffer-Stock Saving
Based on the proposed estimator, we construct a formal test on the sub-unity of the marginal propensity to consume out of permanent income (MPCP) under a nonparametric consumption model and a permanent-transitory model of income dynamics with nonparametric distribution.
GNN Transformation Framework for Improving Efficiency and Scalability
We propose a framework that automatically transforms non-scalable GNNs into precomputation-based GNNs which are efficient and scalable for large-scale graphs.
Scaling Private Deep Learning with Low-Rank and Sparse Gradients
Applying Differentially Private Stochastic Gradient Descent (DPSGD) to training modern, large-scale neural networks such as transformer-based models is a challenging task, as the magnitude of noise added to the gradients at each iteration scales with model dimension, hindering the learning capability significantly.
An Empirical Study of Personalized Federated Learning
Federated learning is a distributed machine learning approach in which a single server and multiple clients collaboratively build machine learning models without sharing datasets on clients.
Predicting Parking Lot Availability by Graph-to-Sequence Model: A Case Study with SmartSantander
Nowadays, so as to improve services and urban areas livability, multiple smart city initiatives are being carried out throughout the world.
Beyond Real-world Benchmark Datasets: An Empirical Study of Node Classification with GNNs
We hope this work offers interesting insights for future research.
Capital and Labor Income Pareto Exponents in the United States, 1916-2019
Accurately estimating income Pareto exponents is challenging due to limitations in data availability and the applicability of statistical methods.
Tuning Parameter-Free Nonparametric Density Estimation from Tabulated Summary Data
Administrative data are often easier to access as tabulated summaries than in the original format due to confidentiality concerns.
Non-Existent Moments of Earnings Growth
Our empirical analysis reveals that population kurtosis, skewness, and variance often do not exist for the conditional distribution of earnings growth.
Similarity Search on Computational Notebooks
In this paper, we propose a similarity search on computational notebooks and develop a new framework for the similarity search.
Standard errors for two-way clustering with serially correlated time effects
We propose improved standard errors and an asymptotic distribution theory for two-way clustered panels.
Slow Movers in Panel Data
Panel data often contain stayers (units with no within-variations) and slow movers (units with little within-variations).
FedMe: Federated Learning via Model Exchange
First, to optimize the model architectures for local data, clients tune their own personalized models by comparing to exchanged models and picking the one that yields the best performance.
Dyadic double/debiased machine learning for analyzing determinants of free trade agreements
Together with the use of Neyman orthogonal scores, this novel cross fitting method enables root-$n$ consistent estimation and inference robustly against dyadic dependence.
Inference in high-dimensional regression models without the exact or $L^p$ sparsity
This paper proposes a new method of inference in high-dimensional regression models and high-dimensional IV regression models.
AIREX: Neural Network-based Approach for Air Quality Inference in Unmonitored Cities
Since existing methods aim to infer air quality of locations only in monitored cities, they do not assume inferring air quality in unmonitored cities.
Fixed-k Tail Regression: New Evidence on Tax and Wealth Inequality from Forbes 400
We develop a novel fixed-k tail regression method that accommodates the unique feature in the Forbes 400 data that observations are truncated from below at the 400th largest order statistic.
Nonparametric Difference-in-Differences in Repeated Cross-Sections with Continuous Treatments
Under our conditions, the time trend can be identified using a control group, as in the binary difference-in-differences literature.
Algorithmic subsampling under multiway clustering
This paper proposes a novel method of algorithmic subsampling (data sketching) for multiway cluster dependent data.
Linear programming approach to nonparametric inference under shape restrictions: with an application to regression kink designs
We develop a novel method of constructing confidence bands for nonparametric regression functions under shape constraints.
Welfare Analysis via Marginal Treatment Effects
Consider a causal structure with endogeneity (i. e., unobserved confoundedness) in empirical data, where an instrumental variable is available.
Inference for high-dimensional exchangeable arrays
We consider inference for high-dimensional separately and jointly exchangeable arrays where the dimensions may be much larger than the sample sizes.
Unconditional Quantile Regression with High Dimensional Data
This paper considers estimation and inference for heterogeneous counterfactual effects with high-dimensional data.
