השוואת שיטות
סקרו את השיטות שבחרתם זו לצד זו; שורות שבהן יש הבדל מודגשות.
| אלגוריתמים לגילוי סיבתי (PC, FCI, LiNGAM)× | תכנון רגרסיה בדידה (RDD)× | כלים דרך ריבועים פחותים בשני שלבים (IV/2SLS)× | |
|---|---|---|---|
| תחום | הסקה סיבתית | הסקה סיבתית | הסקה סיבתית |
| משפחה | Regression model | Regression model | Regression model |
| שנת המקור≠ | 2000 | 2008 | 2009 |
| הוגה השיטה≠ | Spirtes, Glymour & Scheines (PC/FCI); Shimizu et al. (LiNGAM) | Imbens & Lemieux (guide to practice); Cattaneo, Idrobo & Titiunik (practical introduction) | Angrist & Pischke (textbook treatment); Stock & Yogo (weak-instrument theory) |
| סוג≠ | Causal structure learning | Quasi-experimental causal design | Instrumental-variables regression |
| מקור מכונן≠ | Spirtes, P., Glymour, C., & Scheines, R. (2000). Causation, Prediction, and Search (2nd ed.). MIT Press. ISBN: 978-0262194402 | Imbens, G. W., & Lemieux, T. (2008). Regression Discontinuity Designs: A Guide to Practice. Journal of Econometrics, 142(2), 615-635. DOI ↗ | Angrist, J. D. & Pischke, J. S. (2009). Mostly Harmless Econometrics: An Empiricist's Companion. Princeton University Press. ISBN: 978-0691120355 |
| כינויים≠ | PC algorithm, FCI algorithm, LiNGAM, causal structure learning | RDD, regression discontinuity design, sharp RDD, fuzzy RDD | instrumental variables, IV estimation, 2SLS, instrumental variable regression |
| קשורות | 5 | 5 | 5 |
| תקציר≠ | 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. | Regression Discontinuity Design is a quasi-experimental method that identifies a causal effect by locally comparing units just above and just below a cutoff on a continuous assignment (running) variable. Formalised for applied work by Imbens and Lemieux (2008) and developed as a practical framework by Cattaneo, Idrobo, and Titiunik (2020), it estimates a local average treatment effect (LATE) at the threshold. | 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). |
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