2016 Code Plenary Session 1
2016 Code Plenary Session 1 Information Guide
About to 2016 Code Plenary Session 1
Estimation and Evaluation of Optimal Policies. Susan Athey (Stanford University) Escaping from Government and Corporate ... Optimal Design of Experiments on Social Networks. Edo Airoldi (Harvard University) Trustworthy Results: Pitfalls in Online ... Iavor Bojinov – Associate Professor, Harvard Business School Emil Palikot – Assistant Professor, Northeastern. When Randomized Experiments are Plentiful. Dean Eckles (MIT) Insights from Behavioral Economics for Consumer Finance ... Contextual Bandits as Data Collection Algorithms. Susan Athey (Stanford University) Refuted Causal Claims From Observational ... Machine Learning to Test Theories. Sendhil Mullainathan (Harvard University). Inference in Experiments on Networks.
The Necessity for Causation is Overstated. Sendhil Mullainathan (Harvard University) Correlation Rather than Causation? Machine Learning, Causal Inference, and Estimating Heterogeneous Treatment Effects. Jas Sekhon (UC Berkeley) Machine ... Matrix Completion Methods for Causal Panel Data Models. Guido Imbens (Stanford) Digital Experimentation for Multi-Channel ... Ramesh Johari – Professor, Management Science and Engineering, Stanford University Hongseok Namkoong – Assistant ...
Main Features
Developments
Expert Insights
Data is compiled from public records and verified media reports.
Last Updated: June 15, 2026
Summary
Disclaimer: Disclaimer: Details estimates are based on publicly available data, media reports, and financial analysis. Actual numbers may vary.
