arXiv 13 Nov 2020 · Econometrics · 8 citations (OpenAlex)
arXiv:2011.06695 · PDF · DOI · OpenAlex · Extracted main text
In this paper I revisit the interpretation of the linear instrumental variables (IV) estimand as a weighted average of conditional local average treatment effects (LATEs). I focus on a situation in which additional covariates are required for identification while the reduced-form and first-stage regressions may be misspecified due to an implicit homogeneity restriction on the effects of the instrument. I show that the weights on some conditional LATEs are negative and the IV estimand is no longer interpretable as a causal effect under a weaker version of monotonicity, i.e. when there are compliers but no defiers at some covariate values and defiers but no compliers elsewhere. The problem of negative weights disappears in the interacted specification of Angrist and Imbens (1995), which avoids misspecification and seems to be underused in applied work. I illustrate my findings in an application to the causal effects of pretrial detention on case outcomes. In this setting, I reject the stronger version of monotonicity, demonstrate that the interacted instruments are sufficiently strong for consistent estimation using the jackknife methodology, and present several estimates that are economically and statistically different, depending on whether the interacted instruments are used.
appendix boundary found by appendix_command · 67% 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 | Angrist, Joshua D. and Guido W. Imbens (1995) Two-Stage Least Squares Estimation of Average Causal Effects in Models with Variable Treatment Intensity | 1.000 | 47 | 5 | 100% |
| 2 | Chao, John C., Norman R. Swanson, and Tiemen Woutersen (2023) Jackknife Estimation of a Cluster-Sample IV Regression Model with Many Weak Instruments | 1.000 | 7 | 4 | 100% |
| 3 | Abadie, Alberto (2003) Semiparametric Instrumental Variable Estimation of Treatment Response Models | 1.000 | 6 | 3 | 100% |
| 4 | Imbens, Guido W. and Joshua D. Angrist (1994) Identification and Estimation of Local Average Treatment Effects | 1.000 | 5 | 3 | 100% |
| 5 | Kolesár, Michal (2013) Estimation in an Instrumental Variables Model with Treatment Effect Heterogeneity. Unpublished | 0.979 | 16 | 4 | 94% |
| 6 | Stevenson, Megan T (2018) Distortion of Justice: How the Inability to Pay Bail Affects Case Outcomes | 0.905 | 27 | 4 | 74% |
| 7 | Blandhol, Christine, John Bonney, Magne Mogstad, and Alexander Torgo… (2022) When Is TSLS Actually LATE? NBER Working Paper no. 29709 | 0.874 | 11 | 2 | 100% |
| 8 | Angrist, Joshua D. and Jörn-Steffen Pischke (2009) Mostly Harmless Econometrics: An Empiricist's Companion | 0.874 | 5 | 2 | 100% |
| 9 | Blandhol, Christine, John Bonney, Magne Mogstad, and Alexander Torgo… (2025) When Is TSLS Actually LATE? NBER Working Paper no. 29709 | 0.874 | 5 | 2 | 100% |
| 10 | Mikusheva, Anna and Liyang Sun (2022) Inference with Many Weak Instruments | 0.843 | 30 | 5 | 60% |
Showing the top 10 of 88 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Testing Instrument Validity with Covariates | 0.405 | 1 | 1 |
| 2 | Set-Valued Control Functions | 0.405 | 1 | 1 |
| 3 | Better Understanding Triple Differences Estimators | 0.405 | 1 | 1 |
| 4 | Nonlinear Treatment Effects in Shift-Share Designs | 0.405 | 1 | 1 |