Causal research, often known as explanatory research, is a type of research that examines if there lies a cause and effect relationship between two separate variables. This occurs when there is a change in one of the independent variables which, consequently, causes changes in the dependent variable.
Causal research is used to evaluate the effects of specific changes on existing norms, procedures etc. Causal research design examines a condition or a research problem to explain the patterns of interactions between the variables.
The three essential components of causal research are:
- temporal sequence,
- non-spurious association, and
- concomitant variation.
Causal links between variables can be truly demonstrated only with controlled experiments. Experiments test the hypotheses to establish causality in one direction at a time. Since experiments are high in internal validity, cause and effect relationships can be presented with reasonable confidence.
Explanatory or causal research helps organizations understand how their current activities and behaviors will impact them in the future. This is useful in a wide variety of business scenarios like ensuring the outcome of various marketing activities, campaigns, collaterals etc.
List of recommended resources #
For a broad overview #
This manual, developed by the eWater Cooperative Research Centre, & Institute for Applied Ecology School of Resource Environment & Heritage Science, University of Canberra, gives a detailed 8-step causal criteria framework which can be used to integrate information from various data sources.
Written for social scientists and their students, his book by Paul E. Spector provides a compact, clear introduction to the basics of research design.
This overview by UNICEF provides a brief on the role of particular interventions like programmes or policies in producing changes, i.e. on the process of causal attribution.
For in depth understanding #
This book by Judea Pearl provides a comprehensive understanding of the modern analysis of causality. It shows how the research design has grown into a mathematical theory with numerous applications in the fields of statistics, philosophy, economics, artificial intelligence, cognitive science, and the health and social sciences.
The book by Paul Glewwe and Petra Todd gives a holistic understanding of impact evaluation in international development. Chapter 3, in particular, discusses the evaluation problem focusing on correlation and causality.
Case study #
This paper by Salvatore Capasso, Franziska L. Ohnsorge and Shu Yu documents the causal link between financial development and banking sector development to informality. Additionally, it finds that the causality between these factors is stronger in countries with greater trade openness and capital account openness.
Maternal work may affect children on the basis of various causal factors like increased household income, higher control of mothers over available income, greater decision-making power of mothers as they become more economically empowered etc. This review by Maria C. Lo Bue, Elizaveta Perova and Sarah Reynolds summarizes causal evidence on the relationship between maternal work and children’s development.