Martin Emil Jakobsen, Jonas Peters
arXiv 7 May 2020 · Econometrics · publishedEconometrics Journal (2021) · 9 citations (OpenAlex)
arXiv:2005.03353 · PDF · DOI · OpenAlex · Extracted main text
While causal models are robust in that they are prediction optimal under arbitrarily strong interventions, they may not be optimal when the interventions are bounded. We prove that the classical K-class estimator satisfies such optimality by establishing a connection between K-class estimators and anchor regression. This connection further motivates a novel estimator in instrumental variable settings that minimizes the mean squared prediction error subject to the constraint that the estimator lies in an asymptotically valid confidence region of the causal coefficient. We call this estimator PULSE (p-uncorrelated least squares estimator), relate it to work on invariance, show that it can be computed efficiently as a data-driven K-class estimator, even though the underlying optimization problem is non-convex, and prove consistency. We evaluate the estimators on real data and perform simulation experiments illustrating that PULSE suffers from less variability. There are several settings including weak instrument settings, where it outperforms other estimators.
appendix boundary found by appendix_command · 34% 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 | Rothenhäusler, D., N. Meinshausen, P. Bühlmann, and J. Peters (2021) Anchor regression: Heterogeneous data meet causality | 0.969 | 11 | 5 | 91% |
| 2 | Hahn, J., J. Hausman, and G. Kuersteiner (2004) Estimation with weak instruments: Accuracy of higher-order bias and mse approximations | 0.928 | 5 | 3 | 80% |
| 3 | Anderson, T. W. and H. Rubin (1949) Estimation of the parameters of a single equation in a complete system of stochastic equations | 0.843 | 5 | 3 | 60% |
| 4 | Rojas-Carulla, M., B. Schölkopf, R. Turner, and J. Peters (2018) Invariant models for causal transfer learning | 0.737 | 4 | 2 | 75% |
| 5 | Stock, J. H., J. H. Wright, and M. Yogo (2002) A survey of weak instruments and weak identification in generalized method of moments | 0.737 | 3 | 3 | 67% |
| 6 | Theil, H (1958) Economic forecasts and policy | 0.737 | 3 | 2 | 100% |
| 7 | Pearl, J (2009) Causality: Models, Reasoning, and Inference\/ (2nd ed.) | 0.644 | 3 | 2 | 67% |
| 8 | Pfister, N., E. G. William, J. Peters, R. Aebersold, and P. Bühlmann (2021) Stabilizing variable selection and regression | 0.644 | 3 | 2 | 67% |
| 9 | Nagar, A. L (1959) The bias and moment matrix of the general k-class estimators of the parameters in simultaneous equations | 0.644 | 3 | 2 | 67% |
| 10 | Haavelmo, T (1944) The probability approach in econometrics | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 91 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Distributionally Robust Instrumental Variables Estimation | 0.606 | 9 | 3 |
| 2 | A statistician's guide to weak-instrument-robust inference in instrumental variables regression with illustrations in Python | 0.511 | 2 | 1 |