Linganisha mbinu
Pitia mbinu ulizochagua bega kwa bega; safu zinazotofautiana zinaangaziwa.
| DAG Causal Identification× | Ulinganishaji wa Alama ya Mwelekeo× | |
|---|---|---|
| Nyanja≠ | Uhitimisho wa Kisababishi | Takwimu za Utafiti |
| Familia≠ | Regression model | Process / pipeline |
| Mwaka wa asili≠ | 2009 | 1983 |
| Mwanzilishi≠ | Judea Pearl | Paul Rosenbaum and Donald Rubin |
| Aina≠ | Causal identification framework | Method |
| Chanzo asilia≠ | Pearl, J. (2009). Causality: Models, Reasoning, and Inference (2nd ed.). Cambridge University Press. ISBN: 978-0521895606 | Rosenbaum, P. R., & Rubin, D. B. (1983). The central role of the propensity score in observational studies for causal effects. Biometrika, 70(1), 41–55. DOI ↗ |
| Majina mbadala≠ | do-calculus, backdoor adjustment, Pearl causal identification, DAG ile Nedensel Tanımlama (do-calculus) | PSM, propensity score weighting, covariate balance |
| Zinazohusiana≠ | 5 | 3 |
| Muhtasari≠ | DAG causal identification is a framework, developed by Judea Pearl (2009), that encodes causal assumptions as a directed acyclic graph and uses the do-calculus rules to determine whether and how a causal effect can be identified from observational data. It systematically handles confounders, instrumental variables, and backdoor paths. | Propensity score matching (PSM) is a method for reducing confounding bias in observational studies by balancing baseline characteristics between treatment groups, simulating randomization. Developed by Rosenbaum and Rubin (1983), it estimates the probability of receiving treatment given observed covariates, then matches or weights treated and control individuals with similar treatment probabilities. Widely used in medicine, epidemiology, and policy evaluation when randomized trials are infeasible or unethical, enabling estimation of treatment effects while controlling for selection bias. |
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