arXiv 3 Dec 2024 · Econometrics
arXiv:2412.02183 · PDF · DOI · OpenAlex · Extracted main text
This paper investigates the identification and inference of treatment effects in randomized controlled trials with social interactions. Two key network features characterize the setting and introduce endogeneity: (1) latent variables may affect both network formation and outcomes, and (2) the intervention may alter network structure, mediating treatment effects. I make three contributions. First, I define parameters within a post-treatment network framework, distinguishing direct effects of treatment from indirect effects mediated through changes in network structure. I provide a causal interpretation of the coefficients in a linear outcome model. For estimation and inference, I focus on a specific form of peer effects, represented by the fraction of treated friends. Second, in the absence of endogeneity, I establish the consistency and asymptotic normality of ordinary least squares estimators. Third, if endogeneity is present, I propose addressing it through shift-share instrumental variables, demonstrating the consistency and asymptotic normality of instrumental variable estimators in relatively sparse networks. For denser networks, I propose a denoised estimator based on eigendecomposition to restore consistency. Finally, I revisit Prina (2015) as an empirical illustration, demonstrating that treatment can influence outcomes both directly and through network structure changes.
appendix boundary found by appendix_command · 28% of the source is main text. Read the extracted text to check this.
The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Prina, S (2015) Banking the Poor via Savings Accounts: Evidence from a Field Experiment | 1.000 | 19 | 4 | 100% |
| 2 | Pearl, J (2001) Direct and Indirect Effects, in | 0.928 | 5 | 3 | 80% |
| 3 | Adão, R., M. Kolesár, and E. Morales (2019) Shift-Share Designs: Theory and Inference* | 0.928 | 4 | 3 | 100% |
| 4 | Carrell, S. E., B. I. Sacerdote, and J. E. West (2013) From Natural Variation to Optimal Policy? The Importance of Endogenous Peer Group Formation | 0.928 | 4 | 3 | 100% |
| 5 | Borusyak, K. and P. Hull (2023) Nonrandom Exposure to Exogenous Shocks | 0.874 | 6 | 2 | 100% |
| 6 | Hudgens, M. G. and M. E. Halloran (2008) Toward Causal Inference With Interference | 0.874 | 5 | 2 | 100% |
| 7 | Borusyak, K., P. Hull, and X. Jaravel (2022) Quasi-Experimental Shift-Share Research Designs | 0.843 | 5 | 4 | 60% |
| 8 | Banerjee, A., E. Breza, A. G. Chandrasekhar, E. Duflo, M. O. Jackson… (2023) Changes in Social Network Structure in Response to Exposure to Formal Credit Markets | 0.843 | 3 | 3 | 100% |
| 9 | Cai, Y (2022) Linear Regression with Centrality Measures | 0.843 | 3 | 3 | 100% |
| 10 | Li, S. and S. Wager (2022) Random Graph Asymptotics for Treatment Effect Estimation under Network Interference | 0.814 | 13 | 5 | 54% |
Showing the top 10 of 109 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Fixed-Population Causal Inference for Models of Equilibrium | 0.405 | 1 | 1 |
| 2 | What's the Magic Formula Instrument? | 0.405 | 1 | 1 |