手法を比較
選択した手法を並べて確認できます。異なる行はハイライト表示されます。
| 因果発見アルゴリズム (PC, FCI, LiNGAM)× | 操作変数法（IV/2SLS）による推定× | |
|---|---|---|
| 分野 | 因果推論 | 因果推論 |
| 系統 | Regression model | Regression model |
| 提唱年≠ | 2000 | 2009 |
| 提唱者≠ | Spirtes, Glymour & Scheines (PC/FCI); Shimizu et al. (LiNGAM) | Angrist & Pischke (textbook treatment); Stock & Yogo (weak-instrument theory) |
| 種類≠ | Causal structure learning | Instrumental-variables regression |
| 原典≠ | Spirtes, P., Glymour, C., & Scheines, R. (2000). Causation, Prediction, and Search (2nd ed.). MIT Press. ISBN: 978-0262194402 | Angrist, J. D. & Pischke, J. S. (2009). Mostly Harmless Econometrics: An Empiricist's Companion. Princeton University Press. ISBN: 978-0691120355 |
| 別名≠ | PC algorithm, FCI algorithm, LiNGAM, causal structure learning | instrumental variables, IV estimation, 2SLS, instrumental variable regression |
| 関連 | 5 | 5 |
| 概要≠ | Causal discovery is a family of algorithms that automatically learn a directed acyclic graph (DAG) describing causal structure directly from observational data. The constraint-based PC and FCI algorithms were developed by Spirtes, Glymour and Scheines (2000), while the LiNGAM model of Shimizu et al. (2006) exploits linear non-Gaussian structure to orient edges. | IV/2SLS is a two-stage estimation method that recovers the causal effect of an endogenous regressor by isolating the part of its variation driven by an external instrument. It is the workhorse identification strategy in modern applied econometrics, developed at length in Angrist and Pischke's Mostly Harmless Econometrics (2009). |
| ScholarGateデータセット ↗ |
|
|