First, different causal concepts correspond to different subtasks of causality. Nowadays, the number of different causal concepts is increasing exponentially. While research on causality with observational data is burgeoning, more specific subfields of causality are starting to emerge. This has set the stage for causal research with observational data. However, in many cases randomized control trials are unethical or impractical. Therefore, the earliest methods for drawing causal conclusions from data were the randomized controlled trials (RCTs), where units of analysis were randomly assigned treatment or control, eliminating any confounding relation between assignment and outcome. Historically, the fundamental problem of causality, the fact that we cannot observe the outcome under treatment as well as control in a single unit of observation, has long precluded researchers from making causal claims (Holland 1986). Finally, this paper points to further research areas related to the strong assumptions that researchers have glibly adopted to take part in causal discovery, causal identification and causal inference.Ĭausality is a field that has percolated multiple research areas such as medical treatment (Shalit 2020), policy-making (Kreif and DiazOrdaz 2019), social science (Sobel and Legare 2014) epidemiology (Halloran and Struchiner 1995) and cybersecurity (Andrew et al. We show which assumptions are necessary to bridge the gaps between causal discovery, causal identification and causal inference from a parametric and a non-parametric perspective. Therefore, the assumptions underlying each of these causal concepts will be emphasized and their concomitant graphical components will be examined. We will provide the reader with a comprehensive arrangement of assumptions necessary to engage in causal reasoning at the desired level of the hierarchy. This paper aims to disambiguate the different causal concepts that have emerged in causal inference and causal discovery from observational data by attributing them to different levels of Pearl’s Causal Hierarchy. For researchers, it has been increasingly difficult to discern the assumptions they have to abide by in order to glean sound conclusions from causal concepts or methods. Causality has been a burgeoning field of research leading to the point where the literature abounds with different components addressing distinct parts of causality.
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