CriticalThinkingAcademy

The advent and proliferation of computing, programming languages, and software has undoubtedly had a significant impact on the way science is carried out. Simulation modeling is now quite indispensable within the toolkit of the modern scientist. I would go so far to say that you simply cannot do modern science without the aid of a computer in one form or another. This is true for physical sciences and biological sciences as well as social sciences; even non-traditional scientific disciplines like quantitative finance. In fact, most of my initial experience in this during grad school came through studying stochastic processes in financial engineering courses, in addition to Monte Carlo Methods in Bayesian statistics and state space modeling in economics (as well as DSGE models). Since then, I've been interested in simulating social complexity via agent based models. Most modeling cannot be done unless within the context of computer simulation, which requires knowledge of algorithm...

Here we focus on institutional-level research, big data, and the modern scientific enterprise. A corollary to data literacy is understanding that modern science is not merely a matter of isolated individuals discovering facts. The public often imagines science as a person in a lab coat, standing beside a microscope, conducting an experiment, and then announcing a discovery. That picture is not entirely false, but it is dramatically incomplete. Much of modern science is institutional, procedural, standardized, collaborative, and data-supported. This is especially important because many people misunderstand scientific claims by treating them as detached statements. They hear “a study found” and assume the study itself is the unit of truth. They hear “experts recommend” and assume guidance is merely expert opinion. They hear “the data says” and assume data is a clean, self-explanatory object. They hear changing recommendations and assume incompetence or dishonesty. What they often fai...

Now we focus on Causal Inference and Experimentation. The core idea is that: Science is not mainly about finding patterns. It is about figuring out what would happen under different possible worlds. One of the deepest problems in science is that causation is never directly observable. We can observe events, measurements, patterns, and regularities. We can see that two things tend to happen together. We can see that one event came before another. But causation is not simply the fact that two things are associated, nor is it simply the fact that one thing preceded another. A causal claim says something stronger: it says that if one thing had been different, something else would have been different too. This makes causation a strange kind of object. It is not just about what happened. It is about what would have happened under other possible conditions. Suppose a patient takes a drug and recovers. Did the drug cause the recovery? Perhaps. But perhaps the patient would have recovere...