Jann Spiess

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Contact

Affiliations

• Stanford University / Graduate School of Business
  • EDIRC entry
  • location:

Research profile

author of:

• Augmenting Pre-Analysis Plans with Machine Learning (RePEc:aea:apandp:v:109:y:2019:p:71-76)
  by Jens Ludwig & Sendhil Mullainathan & Jann Spiess
• Machine Learning: An Applied Econometric Approach (RePEc:aea:jecper:v:31:y:2017:i:2:p:87-106)
  by Sendhil Mullainathan & Jann Spiess
• Machine-Learning Tests for Effects on Multiple Outcomes (RePEc:arx:papers:1707.01473)
  by Jens Ludwig & Sendhil Mullainathan & Jann Spiess
• Unbiased Shrinkage Estimation (RePEc:arx:papers:1708.06436)
  by Jann Spiess
• Bias Reduction in Instrumental Variable Estimation through First-Stage Shrinkage (RePEc:arx:papers:1708.06443)
  by Jann Spiess
• A Design-Based Perspective on Synthetic Control Methods (RePEc:arx:papers:2101.09398)
  by Lea Bottmer & Guido Imbens & Jann Spiess & Merrill Warnick
• Evidence-Based Policy Learning (RePEc:arx:papers:2103.07066)
  by Jann Spiess & Vasilis Syrgkanis
• Improving Inference from Simple Instruments through Compliance Estimation (RePEc:arx:papers:2108.03726)
  by Stephen Coussens & Jann Spiess
• Revisiting Event Study Designs: Robust and Efficient Estimation (RePEc:arx:papers:2108.12419)
  by Kirill Borusyak & Xavier Jaravel & Jann Spiess
• Unpacking the Black Box: Regulating Algorithmic Decisions (RePEc:arx:papers:2110.03443)
  by Laura Blattner & Scott Nelson & Jann Spiess
• On the Fairness of Machine-Assisted Human Decisions (RePEc:arx:papers:2110.15310)
  by Talia Gillis & Bryce McLaughlin & Jann Spiess
• Algorithmic Assistance with Recommendation-Dependent Preferences (RePEc:arx:papers:2208.07626)
  by Bryce McLaughlin & Jann Spiess
• Optimal Pre-Analysis Plans: Statistical Decisions Subject to Implementability (RePEc:arx:papers:2208.09638)
  by Maximilian Kasy & Jann Spiess
• Double and Single Descent in Causal Inference with an Application to High-Dimensional Synthetic Control (RePEc:arx:papers:2305.00700)
  by Jann Spiess & Guido Imbens & Amar Venugopal
• Machine Learning Who to Nudge: Causal vs Predictive Targeting in a Field Experiment on Student Financial Aid Renewal (RePEc:arx:papers:2310.08672)
  by Susan Athey & Niall Keleher & Jann Spiess
• Personalized Assignment to One of Many Treatment Arms via Regularized and Clustered Joint Assignment Forests (RePEc:arx:papers:2311.00577)
  by Rahul Ladhania & Jann Spiess & Lyle Ungar & Wenbo Wu
• Revisiting Event Study Designs: Robust and Efficient Estimation (RePEc:cpr:ceprdp:17247)
  by Borusyak, Kirill & Jaravel, Xavier & Spiess, Jann
• Megastudies Improve the Impact of Applied Behavioural Science (RePEc:mpr:mprres:60225d44db8d411b9686b344e201373f)
  by Tim Kautz & Katherine L. Milkman & Dena Gromet & Hung Ho & Joseph S. Kay & Timothy W. Lee & Pepi Pandiloski & Yeji Park & Aneesh Rai & Max Bazerman & John Beshears & Lauri Bonacorsi & Colin Camerer &
• A 680,000-person megastudy of nudges to encourage vaccination in pharmacies (RePEc:nas:journl:v:119:y:2022:p:e2115126119)
  by Katherine L. Milkman & Linnea Gandhi & Mitesh S. Patel & Heather N. Graci & Dena M. Gromet & Hung Ho & Joseph S. Kay & Timothy W. Lee & Jake Rothschild & Jonathan E. Bogard & Ilana Brody & Christopher
• Megastudies improve the impact of applied behavioural science (RePEc:nat:nature:v:600:y:2021:i:7889:d:10.1038_s41586-021-04128-4)
  by Katherine L. Milkman & Dena Gromet & Hung Ho & Joseph S. Kay & Timothy W. Lee & Pepi Pandiloski & Yeji Park & Aneesh Rai & Max Bazerman & John Beshears & Lauri Bonacorsi & Colin Camerer & Edward Chang
• Double and Single Descent in Causal Inference with an Application to High-Dimensional Synthetic Control (RePEc:nbr:nberwo:31802)
  by Jann Spiess & Guido Imbens & Amar Venugopal
• Rationalizing Pre-Analysis Plans:Statistical Decisions Subject to Implementability (RePEc:oxf:wpaper:975)
  by Maximilian Kasy & Jann Spiess
• Robust Post-Matching Inference (RePEc:taf:jnlasa:v:117:y:2022:i:538:p:983-995)
  by Alberto Abadie & Jann Spiess