Christoph Breunig, Patrick Burauel
arXiv 13 Jul 2021 · Econometrics · 2 citations (OpenAlex)
arXiv:2107.05936 · PDF · DOI · OpenAlex · Extracted main text
This paper shows that testability of reverse causality is possible even in the absence of exogenous variation, such as in the form of instrumental variables. Instead of relying on exogenous variation, we achieve testability by imposing relatively weak model restrictions and exploiting that a dependence of residual and purported cause is informative about the causal direction. Our main assumption is that the true functional relationship is nonlinear and that error terms are additively separable. We extend previous results by incorporating control variables and allowing heteroskedastic errors. We build on reproducing kernel Hilbert space (RKHS) embeddings of probability distributions to test conditional independence and demonstrate the efficacy in detecting the causal direction in both Monte Carlo simulations and an application to German survey data.
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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 | Mooij, Joris, Peters, Jonas, Janzing, Dominik, Zscheischler, Jakob (2016) Distinguishing cause from effect using observational data: methods and benchmarks | 1.000 | 5 | 3 | 100% |
| 2 | Shimizu, Shohei, Hoyer, Patrik, Kerminen, Antti (2006) A linear non-Gaussian acyclic model for causal discovery | 1.000 | 5 | 3 | 100% |
| 3 | Hoyer, Patrik, Janzing, Dominik, Mooij, Joris M, Peters, Jonas (2009) Nonlinear causal discovery with additive noise models | 0.965 | 10 | 4 | 90% |
| 4 | Zhang, Kun, Peters, J., Janzing, D (2011) Kernel-based Conditional Independence Test and Application in Causal Discovery | 0.693 | 7 | 1 | 100% |
| 5 | Smola, Alexander (2001) Learning with Kernels | 0.511 | 2 | 2 | 50% |
| 6 | Lopez-Paz, David (2016) From Dependence to Causation | 0.511 | 2 | 1 | 100% |
| 7 | Peters, Jonas, Mooij, Joris M, Janzing, Dominik (2014) Causal discovery with continuous additive noise models. | 0.511 | 2 | 1 | 100% |
| 8 | Zhang, Kun (2009) On the Identifiability of the Post-Nonlinear Causal Model | 0.511 | 2 | 1 | 100% |
| 9 | Heinze-Deml, Christina, Peters, Jonas, Munk, Asbjoern Marco Sinius (2019) CondIndTests: Nonlinear Conditional Independence Tests | 0.405 | 1 | 1 | 100% |
| 10 | Blundell, Richard, Horowitz, Joel (2007) A Non Parametric Test of Exogeneity | 0.405 | 1 | 1 | 100% |
Showing the top 10 of 34 scored citations.