scholarly journals Methods of causal inference in contemporary political science

2021 ◽  
pp. 98-115
Author(s):  
Yevgeniy Sedashov
Keyword(s):  
Causal Inference ◽  
Political Science ◽  
Research Design ◽  
Research Problem ◽  
General Description ◽  
Randomized Experiments ◽  
Design Algorithm ◽  
Advantages And Disadvantages ◽  
Quantitative And Qualitative Research ◽  
Inference Methods

This paper serves as an exposition of the causal inference methods that are most popular in political science. Rather than focusing on technical details we present a brief summary of main ideas behind each method with the goal of making them accessible to a broad audience of researchers. We also provide a research design algorithm for each method. First, we focus on a general motivation behind causal inference methods. We discuss how the problem of causality arises in hypothesis testing and describe the relationship between democracy and economic development as a case in point. Second, we give an exposition of a general causality problem within the framework of Rubin Causal Model (RCM). We provide all basic definitions and then demonstrate how the problem of causal inference arise within RCM. Third, we describe the most frequently used methods of causal inference such as randomized experiments, regression discontinuity design, difference-in-difference design, and instrumental variables. For each method we give a reader a general description as well as steps of a research design. We also briefly discuss advantages and disadvantages of each method. Armed with this knowledge, a reader can use it to find the method that is the most appropriate for a research problem at hand. We conclude by arguing that the ideas of causal inference are useful for both quantitative and qualitative research.

THE APPLICATION OF WELLBEING RESEARCH TO REGIONAL LEVEL IN LITHUANIA

2015 ◽  
Author(s):  
Gintarė VAZNONIENĖ ◽  
Bernardas VAZNONIS
Keyword(s):  
Social Sciences ◽  
Regional Development ◽  
Good Practice ◽  
Research Problem ◽  
Regional Level ◽  
Advantages And Disadvantages ◽  
Foreign Countries ◽  
Analysis And Synthesis ◽  
Key Factor ◽  
Development Context

In this article the significance of wellbeing research in the regional level in Lithuania has been analyzed, the advantages and disadvantages of the objective and subjective wellbeing research have been evaluated. The results of the analysis of wellbeing research reveal that the wellbeing research in the regional level is poorly amplified, the wellbeing research in the social sciences is not marked, the wellbeing is investigated in other fields not in social sciences or according to the aims of the researher and more often causes and outcomes of social economical inequality for regional development are emphasized. Scientific studies show that wellbeing research can have big influence for shaping the future of regions because it concerns local people, their choices and overall wellbeing of a particular region. Findings from foreign countries good practice disclose that wellbeing is currently widely used as a key factor and trend for the development policy evaluation. Accordingly in this article big attention is drawn to wellbeing research possible effect for policymakers. It can be concluded that wellbeing research should become an important discussion object in the regional development context because it reveals the situation about people overall wellbeing and particular life domains. The main aim of this article is to analyse the importance of wellbeing research to regional level in Lithuania. The research problem of this article is the fact that the poor experience of wellbeing research in Lithuania insufficiently reveals the wellbeing expression and use in the regional level. In the research common research methods like analysis and synthesis of the scientific literature, analysis of documents and comparative analysis have been employed.

A Survey on Causal Inference

2021 ◽  
Vol 15 (5) ◽  
pp. 1-46
Author(s):  
Liuyi Yao ◽  
Zhixuan Chu ◽  
Sheng Li ◽  
Yaliang Li ◽  
Jing Gao ◽  
...  
Keyword(s):  
Machine Learning ◽  
Causal Inference ◽  
Observational Data ◽  
Causal Effect ◽  
Research Direction ◽  
Estimation Methods ◽  
Potential Outcome ◽  
Outcome Framework ◽  
Benchmark Datasets ◽  
Inference Methods

Causal inference is a critical research topic across many domains, such as statistics, computer science, education, public policy, and economics, for decades. Nowadays, estimating causal effect from observational data has become an appealing research direction owing to the large amount of available data and low budget requirement, compared with randomized controlled trials. Embraced with the rapidly developed machine learning area, various causal effect estimation methods for observational data have sprung up. In this survey, we provide a comprehensive review of causal inference methods under the potential outcome framework, one of the well-known causal inference frameworks. The methods are divided into two categories depending on whether they require all three assumptions of the potential outcome framework or not. For each category, both the traditional statistical methods and the recent machine learning enhanced methods are discussed and compared. The plausible applications of these methods are also presented, including the applications in advertising, recommendation, medicine, and so on. Moreover, the commonly used benchmark datasets as well as the open-source codes are also summarized, which facilitate researchers and practitioners to explore, evaluate and apply the causal inference methods.

Rethinking Comparison

2021 ◽  
Keyword(s):  
Qualitative Research ◽  
Mixed Methods ◽  
Political Science ◽  
Research Design ◽  
Comparative Research ◽  
Comparative Methods ◽  
Political Processes ◽  
Qualitative Research Design

Qualitative comparative methods – and specifically controlled qualitative comparisons – are central to the study of politics. They are not the only kind of comparison, though, that can help us better understand political processes and outcomes. Yet there are few guides for how to conduct non-controlled comparative research. This volume brings together chapters from more than a dozen leading methods scholars from across the discipline of political science, including positivist and interpretivist scholars, qualitative methodologists, mixed-methods researchers, ethnographers, historians, and statisticians. Their work revolutionizes qualitative research design by diversifying the repertoire of comparative methods available to students of politics, offering readers clear suggestions for what kinds of comparisons might be possible, why they are useful, and how to execute them. By systematically thinking through how we engage in qualitative comparisons and the kinds of insights those comparisons produce, these collected essays create new possibilities to advance what we know about politics.

scholarly journals Commentary: Causal Inference for Social Exposures

2019 ◽  
Vol 40 (1) ◽  
pp. 7-21 ◽  
Author(s):  
Jay S. Kaufman
Keyword(s):  
Observational Study ◽  
Causal Inference ◽  
Human Health ◽  
Social Epidemiology ◽  
Social Institutions ◽  
Causal Effects ◽  
Advantages And Disadvantages ◽  
The Cost

Social epidemiology seeks to describe and quantify the causal effects of social institutions, interactions, and structures on human health. To accomplish this task, we define exposures as treatments and posit populations exposed or unexposed to these well-defined regimens. This inferential structure allows us to unambiguously estimate and interpret quantitative causal parameters and to investigate how these may be affected by biases such as confounding. This paradigm has been challenged recently by some critics who favor broadening the exposures that may be studied beyond treatments to also consider states. Defining the exposure protocol of an observational study is a continuum of specificity, and one may choose to loosen this definition, incurring the cost of causal parameters that become commensurately more vague. The advantages and disadvantages of broader versus narrower definitions of exposure are matters of continuing debate in social epidemiology as in other branches of epidemiology.

scholarly journals Alternative causal inference methods in population health research: Evaluating tradeoffs and triangulating evidence

2020 ◽  
Vol 10 ◽  
pp. 100526 ◽  
Author(s):  
Ellicott C. Matthay ◽  
Erin Hagan ◽  
Laura M. Gottlieb ◽  
May Lynn Tan ◽  
David Vlahov ◽  
...  
Keyword(s):  
Causal Inference ◽  
Population Health ◽  
Health Research ◽  
Inference Methods

Research trends in the study of gamification

2019 ◽  
Vol 36 (5) ◽  
pp. 373-380
Author(s):  
Raed S. Alsawaier
Keyword(s):  
Research Design ◽  
Mixed Method ◽  
Quantitative Data ◽  
Empirical Studies ◽  
Research Problem ◽  
Data Sources ◽  
Mixed Method Research ◽  
Content Type ◽  
Holistic Understanding ◽  
Mixed Method Design

Purpose The purpose of this paper is to examine the research design of several publications on the study of gamification and proposes a mixed-method research design for creating a holistic understanding of the gamification phenomenon. It presents an argument in support of combining both qualitative and quantitative data sources through mixed-method design as being equally important in illuminating all aspects of the research problem. Design/methodology/approach The paper covers a number of methodological themes relevant to the study of gamification: research design trends in the study of gamification; the importance of mixed-method design in the study of gamification; methodological challenges; conclusion and recommendations. Findings Majority of the studies on gamification before 2015 are either quantitative or described as mixed method but overly focused on quantitative data sources. However, there is a tendency between 2015 and 2017 to adopt mixed-method design. Research limitations/implications The study does not examine all research done on the topic of gamification but relies on 56 empirical studies reviewed by Hamari, Koivisto, Sarsa (2014) and Seaborn and Fels (2015) between 2009 and 2015. Originality/value The author believes it to be one of the few studies of its kind on proposing a methodological design for the study of gamification as a pedagogical tool.

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