If you are the effect of it, you caused it.

Even when we were kids and did not (yet) know the word causality, probabilistic causal relationships have been sneaking into our lives with remarkable frequency. Think about it for a minute: a seven-year-old you weighing the relative attractiveness of Milky-way against TWIX. What would you pick and why?

Political science is not very different from the chocolate bars' tradeoff - the only distinction is, probably, that the scientists are a tiny bit older and more concerned with the big picture rather than individual eating habits (which does not imply that scientists dislike chocolate - instead, we love it). So, probabilistic causality occurs if a single event or a sequence of repeating episodes like the child’s addition to TWIX bars is somewhat likely to happen, but the probability will lie below 100 percent. Thus, going to the gym or eating a second hamburger for lunch, preferring Joe Biden or Donald Trump (debatable!) - tradeoffs as such are examples of probabilistic but not deterministic outcomes that might generate a significant change in our perceived reality, which we, academics, aim to comprehend with the scientific method.

Identifying and explaining causal mechanisms underlying socio-economic and cultural turbulences are pivotal for social sciences. A researcher should always bear in mind four conditions of causality - association, causal mechanism, time ordering, and no alternative explanations - when approaching complex issues.

The road of causality is long and winding, as Olaf Dekkers once said, which is why one day scholars came up with the so-called “wheel of science” - a plausible narrative for conveying and validating arguments. The wheel is set in motion with a relevant question that needs a defensible answer. To pinpoint the answer, one needs to develop a testable theory that accounts for the association between X and Y - our factors of interest, which we operationalize in hypotheses. After setting up a model that can capture anticipated relationships between X-s and Y-s (remember, the confectionery industry has far more to offer than just Milky-way and TWIX) and designing an appropriate empirical test (will kids always choose TWIX, or can we make Milky-way more lucrative?), one should be able to draw some conclusions accurately. The latter is what we conventionally call inference - a conclusion justified by evidence and logical reasoning.

The next step would be discussing the offered arguments' strengths and weaknesses, showing both sides of the coin. Lastly, we should step out of the comfort zone and conduct robustness checks to show how convincing our results are by confronting them with alternative why-s.

Find out more about finding causality in social sciences by taking a look at my presentation from Fall 2020.