Methods for Causal Inference in Marketing is a systematic review of recent developments in causal inference methods and their applications within the marketing field. For each causal inference method, five recently published academic papers in marketing research that employ these methods are discussed. In addition, this monograph provides simplified code for developing simulated data (using Python) and hypothetical examples of data analysis (using Stata). This addition will enable marketing researchers to practice several methods of causal analysis. Sections 1-5 elucidate the fundamental principles of causal inference. Subsequent sections (beginning from Section 6) delve into the details of a selection of papers that utilize various methods. These encompass (i) well-established techniques, such as Differences-In-Differences, Instrumental Variable, Regression Discontinuity, Synthetic Control Method, and Propensity Score Method, and (ii) emerging methodologies of Factor Model and Augmented Differences-In-Differences, Forward Differences-In-Differences, and Bayesian methods for causal inference. Further, this monograph reviews how machine learning methods enhance causal inference and includes several important and useful references not reviewed. This monograph serves as a useful resource both to current and future researchers in marketing.
Methods for Causal Inference in Marketing is a systematic review of recent developments in causal inference methods and their applications within the marketing field. For each causal inference method, five recently published academic papers in marketing research that employ these methods are discussed. In addition, this monograph provides simplified code for developing simulated data (using Python) and hypothetical examples of data analysis (using Stata). This addition will enable marketing researchers to practice several methods of causal analysis. Sections 1-5 elucidate the fundamental principles of causal inference. Subsequent sections (beginning from Section 6) delve into the details of a selection of papers that utilize various methods. These encompass (i) well-established techniques, such as Differences-In-Differences, Instrumental Variable, Regression Discontinuity, Synthetic Control Method, and Propensity Score Method, and (ii) emerging methodologies of Factor Model and Augmented Differences-In-Differences, Forward Differences-In-Differences, and Bayesian methods for causal inference. Further, this monograph reviews how machine learning methods enhance causal inference and includes several important and useful references not reviewed. This monograph serves as a useful resource both to current and future researchers in marketing.
