{"title":"The Causal Cookbook: Recipes for Propensity Scores, G-Computation, and Doubly Robust Standardization","authors":"A. Chatton, J. Rohrer","doi":"10.1177/25152459241236149","DOIUrl":null,"url":null,"abstract":"Recent developments in the causal-inference literature have renewed psychologists’ interest in how to improve causal conclusions based on observational data. A lot of the recent writing has focused on concerns of causal identification (under which conditions is it, in principle, possible to recover causal effects?); in this primer, we turn to causal estimation (how do researchers actually turn the data into an effect estimate?) and modern approaches to it that are commonly used in epidemiology. First, we explain how causal estimands can be defined rigorously with the help of the potential-outcomes framework, and we highlight four crucial assumptions necessary for causal inference to succeed (exchangeability, positivity, consistency, and noninterference). Next, we present three types of approaches to causal estimation and compare their strengths and weaknesses: propensity-score methods (in which the independent variable is modeled as a function of controls), g-computation methods (in which the dependent variable is modeled as a function of both controls and the independent variable), and doubly robust estimators (which combine models for both independent and dependent variables). A companion R Notebook is available at github.com/ArthurChatton/CausalCookbook. We hope that this nontechnical introduction not only helps psychologists and other social scientists expand their causal toolbox but also facilitates communication across disciplinary boundaries when it comes to causal inference, a research goal common to all fields of research.","PeriodicalId":55645,"journal":{"name":"Advances in Methods and Practices in Psychological Science","volume":null,"pages":null},"PeriodicalIF":15.6000,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Advances in Methods and Practices in Psychological Science","FirstCategoryId":"102","ListUrlMain":"https://doi.org/10.1177/25152459241236149","RegionNum":1,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"PSYCHOLOGY","Score":null,"Total":0}
引用次数: 0
Abstract
Recent developments in the causal-inference literature have renewed psychologists’ interest in how to improve causal conclusions based on observational data. A lot of the recent writing has focused on concerns of causal identification (under which conditions is it, in principle, possible to recover causal effects?); in this primer, we turn to causal estimation (how do researchers actually turn the data into an effect estimate?) and modern approaches to it that are commonly used in epidemiology. First, we explain how causal estimands can be defined rigorously with the help of the potential-outcomes framework, and we highlight four crucial assumptions necessary for causal inference to succeed (exchangeability, positivity, consistency, and noninterference). Next, we present three types of approaches to causal estimation and compare their strengths and weaknesses: propensity-score methods (in which the independent variable is modeled as a function of controls), g-computation methods (in which the dependent variable is modeled as a function of both controls and the independent variable), and doubly robust estimators (which combine models for both independent and dependent variables). A companion R Notebook is available at github.com/ArthurChatton/CausalCookbook. We hope that this nontechnical introduction not only helps psychologists and other social scientists expand their causal toolbox but also facilitates communication across disciplinary boundaries when it comes to causal inference, a research goal common to all fields of research.
期刊介绍:
In 2021, Advances in Methods and Practices in Psychological Science will undergo a transition to become an open access journal. This journal focuses on publishing innovative developments in research methods, practices, and conduct within the field of psychological science. It embraces a wide range of areas and topics and encourages the integration of methodological and analytical questions.
The aim of AMPPS is to bring the latest methodological advances to researchers from various disciplines, even those who are not methodological experts. Therefore, the journal seeks submissions that are accessible to readers with different research interests and that represent the diverse research trends within the field of psychological science.
The types of content that AMPPS welcomes include articles that communicate advancements in methods, practices, and metascience, as well as empirical scientific best practices. Additionally, tutorials, commentaries, and simulation studies on new techniques and research tools are encouraged. The journal also aims to publish papers that bring advances from specialized subfields to a broader audience. Lastly, AMPPS accepts Registered Replication Reports, which focus on replicating important findings from previously published studies.
Overall, the transition of Advances in Methods and Practices in Psychological Science to an open access journal aims to increase accessibility and promote the dissemination of new developments in research methods and practices within the field of psychological science.