The probability of simple versus complex causal models in causal analyses (original) (raw)

Statistical modeling and causality in social sciences

Institute de Statistique Discussion Paper, 2006

Philosophers and statisticians have been debating on causality for a long time. However, these discussions have been led quite independently from each other. An objective of this paper is to restore a fruitful dialogue between philosophy and statistics. As is well known, at the beginning of the 20th century, some philosophers and statisticians dismissed the concept of causality altogether. It will suffice to mention Bertrand Russell (1913) and Karl Pearson (1911). Almost a hundred years later, causality still represents a central topic ...

A Modern Approach to the Fundamental Problem of Causal Inference Andrea Berdondini

The fundamental problem of causal inference defines the impossibility of associating a causal link to a correlation, in other words: correlation does not prove causality. This problem can be understood from two points of view: experimental and statistical. The experimental approach tells us that this problem arises from the impossibility of simultaneously observing an event both in the presence and absence of a hypothesis. The statistical approach, on the other hand, suggests that this problem stems from the error of treating tested hypotheses as independent of each other. Modern statistics tends to place greater emphasis on the statistical approach because, compared to the experimental point of view, it also shows us a way to solve the problem. Indeed, when testing many hypotheses, a composite hypothesis is constructed that tends to cover the entire solution space. Consequently, the composite hypothesis can be fitted to any data set by generating a random correlation. Furthermore, the probability that the correlation is random is equal to the probability of obtaining the same result by generating an equivalent number of random hypotheses.

Assumptions and Interventions of Probabilistic Causal Models

2002

Causality is an intriguing but controversial topic in philosophy, statistics, as well as educational and psychological research. By supporting Causal Markov Condition and Faithfulness Condition, Clark Glymour attempted to draw causal inferences from structural equation modeling. According to Glymour, in order to make causal interpretation of non-experimental data, the researcher must have some type of manipulation, rather than conditioning, of variables. The Causal Markov Condition and its sister, the common cause principle, provide the assumptions to structure relationships among variables in the path model and to load different variables into common latent constructs in the factor model. In addition, the Faithfulness Condition rules out those models in which statistical independence relations follow as a result of special coincidences among the parameter values. The arguments against these assumptions by Nancy Cartwright as well as those for these assumptions by James Woodward will be evaluated in this paper.

Review of the book “Causal Inference for Statistics, Social, and Biomedical Sciences” by G.W. Imbens and D.B. Rubin

Observational Studies

Research questions that motivate most studies in statistics-based sciences are causal in nature. Economists and social scientists are typically interested in estimating causal effects rather than mere associations between variables (e.g., the effects of training programs on subsequent labor market histories); the same is true for epidemiologists and medical doctors (e.g., is smoking causing lung cancer? what is the effect of pollution on health outcomes?).

Statistical Modelling and Causality

2006

Philosophers and statisticians have been debating on causality for a long time. However, these discussions have been led quite independently from each other. An objective of this paper is to restore a fruitful dialogue between them. As is well known, at the beginning of the 20th century, some philosophers and statisticians dismissed the concept of causality altogether. It will suffice to mention Bertrand Russell (1913) and Karl Pearson (1911). Almost a hundred years later, causality still represents a central topic both in philosophy and ...

The role of causal criteria in causal inferences: Bradford Hill's "aspects of association

Epidemiologic Perspectives & Innovations, 2009

As noted by Wesley Salmon and many others, causal concepts are ubiquitous in every branch of theoretical science, in the practical disciplines and in everyday life. In the theoretical and practical sciences especially, people often base claims about causal relations on applications of statistical methods to data. However, the source and type of data place important constraints on the choice