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A Simple Approximation to the Distribution of the Ridge 回归 (Regression) Estimator
A Simple Approximation to the Distribution of the Ridge Regression Estimator
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Debiased Machine 学习 (Learning): Identification, 估计 (Estimation), and Shape Constraints
Debiased Machine Learning: Identification, Estimation, and Shape Constraints
👁 99 📚 7
Stochastic Potential Choices and Outcomes
👁 62 📚 30
Identifying Treatment and Spillover Effects with Control-Based and Forecast-Based Counterfactuals
👁 153 📚 25
Vector Search As Nearest Neighbor Matching: RAG-based Policy 学习 (Learning) in Causal 推断 (Inference)
Vector Search As Nearest Neighbor Matching: RAG-based Policy Learning in Causal Inference
👁 54 📚 9
mnorm: An R Package for Calculation and Differentiation of Conditional Multivariate Normal Densities...
👁 43 📚 21
Aggregation Bias in Proxy Measurement: Nighttime Lights and Local Economic Activity
👁 43 📚 23
Robust 推断 (Inference) for Weighted Estimands
Robust Inference for Weighted Estimands
👁 29 📚 11
Finite-Population 推断 (Inference) for Heterogeneity in Many-Group Synthetic Difference-in-Differences
Finite-Population Inference for Heterogeneity in Many-Group Synthetic Difference-in-Differences
👁 160 📚 24
A Design-Based Approach to Testing and 推断 (Inference) in (Quasi-)Experiments with Spillovers
A Design-Based Approach to Testing and Inference in (Quasi-)Experiments with Spillovers
👁 129 📚 12
A Machine-学习 (Learning)-Compatible Omnibus Test for Treatment Effect Heterogeneity
A Machine-Learning-Compatible Omnibus Test for Treatment Effect Heterogeneity
👁 55 📚 21
推断 (Inference) for Group Interaction Experiments
Inference for Group Interaction Experiments
👁 78 📚 5
Time-Varying 模型 (Model) Averaging of Multi-layer Network Vector Autoregressions
Time-Varying Model Averaging of Multi-layer Network Vector Autoregressions
👁 215 📚 0
Embedding Foundation 模型 (Model) 预测 (Prediction)s in Discrete-Choice 模型 (Model)s with Structural Guar...
Embedding Foundation Model Predictions in Discrete-Choice Models with Structural Guarantees
👁 51 📚 17
Choosing What to Calibrate and What to Estimate in Structural 模型 (Model)s
Choosing What to Calibrate and What to Estimate in Structural Models
👁 162 📚 26
Bounds for Standard Errors in Combined 数据 (Data)
Bounds for Standard Errors in Combined Data
👁 149 📚 3
Variance or Standard Deviation? Shell Geometry and Global-Scale Priors in High-Dimensional Shrinkage
👁 152 📚 20
Institutions, Inputs, and Agricultural Growth in China:Revisiting Several Controversies, 1949--1986
👁 86 📚 8
Choosing A Headline Estimand from Matching, DID, and Hybrid Designs: A Minimax-Regret Approach
👁 148 📚 25
Generative Predictive Distributions for Time Series
👁 39 📚 25
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