পদ্ধতির তুলনা করুন
নির্বাচিত পদ্ধতিগুলো পাশাপাশি পর্যালোচনা করুন; যে সারিগুলোয় পার্থক্য আছে সেগুলো চিহ্নিত করা হয়।
| ডাইনামিক কাউন্টারফ্যাকচুয়াল ইমপ্যাক্ট ইভালুয়েশন× | প্যানেল ইভেন্ট স্টাডি× | |
|---|---|---|
| ক্ষেত্র | কার্যকারণ অনুমান | কার্যকারণ অনুমান |
| পরিবার | Regression model | Regression model |
| উদ্ভবের বছর≠ | 1986–2009 | 1990s–2020s (modern panel formulation) |
| প্রবর্তক≠ | Robins (1986); Lechner (2009) for sequential treatment settings | Formalized by Freyaldenhoven, Hansen, Perez-Orive & Shapiro (2021); widely applied in finance (Fama et al. 1969) and policy evaluation |
| ধরন≠ | Causal inference / program evaluation | Quasi-experimental / causal panel design |
| মৌলিক উৎস≠ | Robins, J. M. (1986). A new approach to causal inference in mortality studies with a sustained exposure period — application to control of the healthy worker survivor effect. Mathematical Modelling, 7(9-12), 1393-1512. DOI ↗ | Freyaldenhoven, S., Hansen, C., Perez-Orive, J., & Shapiro, J. M. (2021). Visualization, Identification, and Estimation in the Linear Panel Event-Study Design. NBER Working Paper 29170. National Bureau of Economic Research. link ↗ |
| অপর নাম | dynamic CIE, dynamic treatment evaluation, time-varying counterfactual analysis, longitudinal counterfactual evaluation | event-study regression, dynamic DiD, relative-time regression, distributed-lag panel model |
| সম্পর্কিত≠ | 6 | 4 |
| সারসংক্ষেপ≠ | Dynamic Counterfactual Impact Evaluation (dynamic CIE) extends standard counterfactual program evaluation to settings where treatment is assigned sequentially across multiple periods. Rather than comparing a single treated versus untreated state, it estimates the causal effect of entire treatment trajectories or regimes, accounting for how intermediate outcomes and time-varying covariates feed back into subsequent treatment decisions. | A panel event study estimates the dynamic causal effect of a treatment or policy by regressing an outcome on a full set of relative-time indicators — one for each period before and after the event — while controlling for unit and time fixed effects. The resulting coefficient plot shows how the treated units diverged from untreated units at each point in calendar time relative to their treatment date, making both pre-treatment trend violations and post-treatment effect trajectories immediately visible. |
| ScholarGateডেটাসেট ↗ |
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