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Found 9 papers, 9 papers with code
Gender gaps in frontier entrepreneurship? Evidence from 1901 Oklahoma land lottery winners
For a sample of winners linked to the 1910 Census, I find that male winners have higher median dose-responses compared to female winners in terms of farm or home ownership.
Amnesty Policy and Elite Persistence in the Postbellum South: Evidence from a Regression Discontinuity Design
Results on a sample of Reconstruction convention delegates show that exclusion from amnesty significantly decreased the likelihood of ex-post officeholding.
Are deep learning models superior for missing data imputation in large surveys? Evidence from an empirical comparison
We conduct extensive simulation studies based on a subsample of the American Community Survey to compare the repeated sampling properties of four machine learning based MI methods: MICE with classification trees, MICE with random forests, generative adversarial imputation networks, and multiple imputation using denoising autoencoders.
Adversarial Machine Learning: Bayesian Perspectives
Adversarial Machine Learning (AML) is emerging as a major field aimed at protecting machine learning (ML) systems against security threats: in certain scenarios there may be adversaries that actively manipulate input data to fool learning systems.
State-Building through Public Land Disposal? An Application of Matrix Completion for Counterfactual Prediction
This paper examines how homestead policies, which opened vast frontier lands for settlement, influenced the development of American frontier states.
Estimating population average treatment effects from experiments with noncompliance
This paper extends a method of estimating population average treatment effects to settings with noncompliance.
Methodology Econometrics Applications
Character-Based Handwritten Text Transcription with Attention Networks
When the sequence alignment is one-to-one, softmax attention is able to learn a more precise alignment at each step of the decoding, whereas the alignment generated by sigmoid attention is much less precise.
RNN-based counterfactual prediction, with an application to homestead policy and public schooling
This paper proposes a method for estimating the effect of a policy intervention on an outcome over time.
Missing Data Imputation for Supervised Learning
Missing data imputation can help improve the performance of prediction models in situations where missing data hide useful information.
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