{"title":"Combining process tracing and synthetic control method: Bridging two ways of making causal inference in evaluation research","authors":"F. Podestà","doi":"10.1177/13563890221139511","DOIUrl":null,"url":null,"abstract":"This article discusses potential ways of combining two methods of evaluation in single-case studies: the synthetic control method and the process tracing method. Both are designed to examine certain events/programmes that take place in given cases but view these events/programmes from different causal perspectives. Seeing an event/programme as a cause, synthetic control estimates its impact on one or more outcomes. Conversely, starting from a certain outcome, process tracing uncovers the causes responsible. One can start from the causal explanation reached via one of the two methods and then proceed to examine that explanation through the other method. Once the causes of an outcome are traced via a process tracing analysis, that account can be validated by estimating the effects of those causes via synthetic control. Equally, once the impact of a certain event is estimated through synthetic control, causal mechanisms traceable via process tracing can be exploited to refine that impact evaluation.","PeriodicalId":19964,"journal":{"name":"Performance Evaluation","volume":"39 1","pages":"50 - 66"},"PeriodicalIF":1.0000,"publicationDate":"2022-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Performance Evaluation","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1177/13563890221139511","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"COMPUTER SCIENCE, HARDWARE & ARCHITECTURE","Score":null,"Total":0}
引用次数: 0
Abstract
This article discusses potential ways of combining two methods of evaluation in single-case studies: the synthetic control method and the process tracing method. Both are designed to examine certain events/programmes that take place in given cases but view these events/programmes from different causal perspectives. Seeing an event/programme as a cause, synthetic control estimates its impact on one or more outcomes. Conversely, starting from a certain outcome, process tracing uncovers the causes responsible. One can start from the causal explanation reached via one of the two methods and then proceed to examine that explanation through the other method. Once the causes of an outcome are traced via a process tracing analysis, that account can be validated by estimating the effects of those causes via synthetic control. Equally, once the impact of a certain event is estimated through synthetic control, causal mechanisms traceable via process tracing can be exploited to refine that impact evaluation.
期刊介绍:
Performance Evaluation functions as a leading journal in the area of modeling, measurement, and evaluation of performance aspects of computing and communication systems. As such, it aims to present a balanced and complete view of the entire Performance Evaluation profession. Hence, the journal is interested in papers that focus on one or more of the following dimensions:
-Define new performance evaluation tools, including measurement and monitoring tools as well as modeling and analytic techniques
-Provide new insights into the performance of computing and communication systems
-Introduce new application areas where performance evaluation tools can play an important role and creative new uses for performance evaluation tools.
More specifically, common application areas of interest include the performance of:
-Resource allocation and control methods and algorithms (e.g. routing and flow control in networks, bandwidth allocation, processor scheduling, memory management)
-System architecture, design and implementation
-Cognitive radio
-VANETs
-Social networks and media
-Energy efficient ICT
-Energy harvesting
-Data centers
-Data centric networks
-System reliability
-System tuning and capacity planning
-Wireless and sensor networks
-Autonomic and self-organizing systems
-Embedded systems
-Network science