Achieving Reliable Causal Inference with Data-Mined Variables: A Random Forest Approach to the Measurement Error Problem
- Mochen Yang ,
Corresponding Author
Mochen Yang
[email protected]https://orcid.org/0000-0001-5101-9041
Department of Information and Decision Sciences, Carlson School of Management, University of Minnesota, Minneapolis, Minnesota 55455;
- Edward McFowland, III ,
Edward McFowland, III
[email protected]https://orcid.org/0000-0001-5249-7117
Department of Technology and Operations Management, Harvard Business School, Boston, Massachusetts 02163;
- Gordon Burtch ,
Gordon Burtch
[email protected]https://orcid.org/0000-0001-9798-1113
Department of Information Systems, Questrom School of Business, Boston University, Boston, Massachusetts 02215
- Gediminas Adomavicius
Gediminas Adomavicius
[email protected]https://orcid.org/0000-0001-5251-5098
Department of Information and Decision Sciences, Carlson School of Management, University of Minnesota, Minneapolis, Minnesota 55455;
Corresponding Author
Mochen Yang
[email protected]https://orcid.org/0000-0001-5101-9041
Department of Information and Decision Sciences, Carlson School of Management, University of Minnesota, Minneapolis, Minnesota 55455;
Edward McFowland, III
[email protected]https://orcid.org/0000-0001-5249-7117
Department of Technology and Operations Management, Harvard Business School, Boston, Massachusetts 02163;
Gordon Burtch
[email protected]https://orcid.org/0000-0001-9798-1113
Department of Information Systems, Questrom School of Business, Boston University, Boston, Massachusetts 02215
Gediminas Adomavicius
[email protected]https://orcid.org/0000-0001-5251-5098
Department of Information and Decision Sciences, Carlson School of Management, University of Minnesota, Minneapolis, Minnesota 55455;
