Market Responses to Genuine Versus Strategic Generosity: An Empirical Examination of NFT Charity Fundraisers

no code implementations • 22 Jan 2024 • Chen Liang, Murat Tunc, Gordon Burtch

Questions may arise about the motivations of donors in these charity fundraisers, resulting in a negative social image.

EnsembleIV: Creating Instrumental Variables from Ensemble Learners for Robust Statistical Inference

no code implementations • 6 Mar 2023 • Gordon Burtch, Edward McFowland III, Mochen Yang, Gediminas Adomavicius

Despite increasing popularity in empirical studies, the integration of machine learning generated variables into regression models for statistical inference suffers from the measurement error problem, which can bias estimation and threaten the validity of inferences.

regression valid

Achieving Reliable Causal Inference with Data-Mined Variables: A Random Forest Approach to the Measurement Error Problem

no code implementations • 19 Dec 2020 • Mochen Yang, Edward McFowland III, Gordon Burtch, Gediminas Adomavicius

The random forest algorithm performs best when comprised of a set of trees that are individually accurate in their predictions, yet which also make 'different' mistakes, i. e., have weakly correlated prediction errors.

BIG-bench Machine Learning Causal Inference +1