Search Results for author: Jann Spiess

Found 10 papers, 1 papers with code

Rationalizing Pre-Analysis Plans: Statistical Decisions Subject to Implementability

no code implementations20 Aug 2022 Maximilian Kasy, Jann Spiess

Pre-analysis plans (PAPs) are a potential remedy to the publication of spurious findings in empirical research, but they have been criticized for their costs and for preventing valid discoveries.

Algorithmic Assistance with Recommendation-Dependent Preferences

no code implementations16 Aug 2022 Bryce McLaughlin, Jann Spiess

But when a decision-maker obtains a recommendation, they may not only react to the information.

On the Fairness of Machine-Assisted Human Decisions

no code implementations28 Oct 2021 Talia Gillis, Bryce McLaughlin, Jann Spiess

We show in a formal model that the inclusion of a biased human decision-maker can revert common relationships between the structure of the algorithm and the qualities of resulting decisions.


Unpacking the Black Box: Regulating Algorithmic Decisions

no code implementations5 Oct 2021 Laura Blattner, Scott Nelson, Jann Spiess

We characterize optimal oversight of algorithms in a world where an agent designs a complex prediction function but a principal is limited in the amount of information she can learn about the prediction function.

Revisiting Event Study Designs: Robust and Efficient Estimation

1 code implementation27 Aug 2021 Kirill Borusyak, Xavier Jaravel, Jann Spiess

We develop a framework for difference-in-differences designs with staggered treatment adoption and heterogeneous causal effects.


Improving Inference from Simple Instruments through Compliance Estimation

no code implementations8 Aug 2021 Stephen Coussens, Jann Spiess

In the case where both the treatment and instrument are binary and the instrument is independent of baseline covariates, we study weighting each observation according to its estimated compliance (that is, its conditional probability of being affected by the instrument), which we motivate from a (constrained) solution of the first-stage prediction problem implicit to IV.


Evidence-Based Policy Learning

no code implementations12 Mar 2021 Jann Spiess, Vasilis Syrgkanis

The past years have seen seen the development and deployment of machine-learning algorithms to estimate personalized treatment-assignment policies from randomized controlled trials.

A Design-Based Perspective on Synthetic Control Methods

no code implementations23 Jan 2021 Lea Bottmer, Guido Imbens, Jann Spiess, Merrill Warnick

Here we study SC methods from a design-based perspective, assuming a model for the selection of the treated unit(s) and period(s).

Machine-Learning Tests for Effects on Multiple Outcomes

no code implementations5 Jul 2017 Jens Ludwig, Sendhil Mullainathan, Jann Spiess

In this paper we present tools for applied researchers that re-purpose off-the-shelf methods from the computer-science field of machine learning to create a "discovery engine" for data from randomized controlled trials (RCTs).

BIG-bench Machine Learning

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