Cause-Effect Inference by Comparing Regression Errors

By Patrick Blöbaum, Dominik Janzing, Takashi Washio, Shohei Shimizu, Bernhard Schölkopf. 2018

93 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Bernhard Schölkopf
2. Yoshua Bengio
3. Eric P. Xing
4. David Lopez-Paz
5. Dacheng Tao
6. Kun Zhang
7. Diviyan Kalainathan
8. Sergey Levine

Citations in the corpus

• Learning Sparse Nonparametric DAGs (2019, background): “, 2014; Blöbaum et al., 2018; Mooij et al., 2016), post-nonlinear models (Zhang and Hyvärinen, 2009; Zhang et al., 2016) and general nonlinear SEM (Monti et al., 2019; Goudet et al., 2018; Kalainathan et al., 2018; Sgouritsa et al., 2015). Recently, Yu et al. (2019) proposed to use graph neural netw”
• D’ya Like DAGs? A Survey on Structure Learning and Causal Discovery (2021, unknown): “CausalMosaic[263]2020 AEQ[63]2020 CAREFL[126]2020 RECI[21]2018 IGCI[112]2014 ANM[100]2020 69[126] 61[263] 52[263] Table 4.”
• Structural Agnostic Modeling: Adversarial Learning of Causal Graphs (2018, methodology): “• the RECI algorithm (Blöbaum et al., 2018) comparing regression errors.”
• Causal Discovery with General Non-Linear Relationships using Non-Linear ICA (2019, methodology): “Related to the RESIT algorithm is the Regression Error Causal Inference (RECI) algorithm (Bl¨obaum et al., 2018), which proposes measures of causal direction based on the magnitude of (non-linear) regression errors.”