There is no unified theory of causality in the sciences and in philosophy. In this paper, we focus on a particular framework, called Structural Causal Modelling (SCM), as one possible perspective in quantitative social science research. We explain how this methodology provides a fruitful basis for causal analysis in social research, for hypothesising, modelling, and testing explanatory mechanisms. This framework is not based on a system of equations, but on an analysis of multivariate distributions. In particular, the modelling stage is essentially distribution-free. Adopting an SCM approach means endorsing a particular view on modelling in general (the hypothetico-deductive methodology), and a specific stance on exogeneity (namely as a con...
This paper provides an overview of structural modelling in its close relation to explanation and cau...
This review presents empirical researchers with recent advances in causal inference, and stresses th...
A shared problem across the sciences is to make sense of correlational data coming from observations...
There is no unified theory of causality in the sciences and in philosophy. In this paper, we focus o...
The objective of this paper is to present a short overview of the Structural Causal Modelling (SCM) ...
This paper deals with causal analysis in the social sciences. We first present a conceptual framewo...
This paper deals with causal analysis in the social sciences. We first present a conceptual framewor...
This paper examines different approaches for assessing causality as typically followed in econometri...
This paper examines different approaches for assessing causality as typically followed in econometri...
This paper examines different approaches for assessing causality as typically followed in econometri...
This chapter deals with causal explanation in quantitative‐oriented social sciences. In the framewor...
Social scientists ’ interest in causal effects is as old as the social sciences. Attention to the ph...
The intrinsic schism between causal and associational relations presents profound ethical and method...
The anti-causal prophecies of last century have been disproved. Causality is neither a ‘relic of a b...
This paper summarizes recent advances in causal inference and underscores the paradigmatic shifts th...
This paper provides an overview of structural modelling in its close relation to explanation and cau...
This review presents empirical researchers with recent advances in causal inference, and stresses th...
A shared problem across the sciences is to make sense of correlational data coming from observations...
There is no unified theory of causality in the sciences and in philosophy. In this paper, we focus o...
The objective of this paper is to present a short overview of the Structural Causal Modelling (SCM) ...
This paper deals with causal analysis in the social sciences. We first present a conceptual framewo...
This paper deals with causal analysis in the social sciences. We first present a conceptual framewor...
This paper examines different approaches for assessing causality as typically followed in econometri...
This paper examines different approaches for assessing causality as typically followed in econometri...
This paper examines different approaches for assessing causality as typically followed in econometri...
This chapter deals with causal explanation in quantitative‐oriented social sciences. In the framewor...
Social scientists ’ interest in causal effects is as old as the social sciences. Attention to the ph...
The intrinsic schism between causal and associational relations presents profound ethical and method...
The anti-causal prophecies of last century have been disproved. Causality is neither a ‘relic of a b...
This paper summarizes recent advances in causal inference and underscores the paradigmatic shifts th...
This paper provides an overview of structural modelling in its close relation to explanation and cau...
This review presents empirical researchers with recent advances in causal inference, and stresses th...
A shared problem across the sciences is to make sense of correlational data coming from observations...