Search Results for author: Eric Auerbach

Found 7 papers, 3 papers with code

Exposure effects are not automatically useful for policymaking

no code implementations11 Jan 2024 Eric Auerbach, Jonathan Auerbach, Max Tabord-Meehan

We thank Savje (2023) for a thought-provoking article and appreciate the opportunity to share our perspective as social scientists.

Identifying Socially Disruptive Policies

1 code implementation26 Jun 2023 Eric Auerbach, Yong Cai

Social disruption occurs when a policy creates or destroys many network connections between agents.

Heterogeneous Treatment Effects for Networks, Panels, and other Outcome Matrices

1 code implementation2 May 2022 Eric Auerbach, Yong Cai

We then propose a new matrix analog of quantile treatment effects that is given by a difference in the eigenvalues.

Experimental Design

Identification and Estimation of a Partially Linear Regression Model using Network Data: Inference and an Application to Network Peer Effects

no code implementations20 May 2021 Eric Auerbach

This paper provides additional results relevant to the setting, model, and estimators of Auerbach (2019a).

The Local Approach to Causal Inference under Network Interference

no code implementations9 May 2021 Eric Auerbach, Max Tabord-Meehan

We propose a new nonparametric modeling framework for causal inference when outcomes depend on how agents are linked in a social or economic network.

Causal Inference valid

Recovering Network Structure from Aggregated Relational Data using Penalized Regression

1 code implementation16 Jan 2020 Hossein Alidaee, Eric Auerbach, Michael P. Leung

Breza et al. (2017) propose aggregated relational data (ARD) as a low-cost substitute that can be used to recover the structure of a latent social network when it is generated by a specific parametric random effects model.

regression

Identification and Estimation of a Partially Linear Regression Model using Network Data

no code implementations22 Mar 2019 Eric Auerbach

In this paper, I describe the model, formalize this intuition, and provide consistent estimators for the parameters of the regression model.

regression

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