By Samantha Kleinberg
Can ingesting espresso support humans reside longer? What makes a stock's rate cross up? Why did you get the flu? Causal questions like those come up frequently, yet most folks most likely haven't notion deeply approximately tips to solution them. This publication is helping you consider causality in a based method: what's a reason, what are reasons solid for, and what's compelling proof of causality? writer Samantha Kleinberg indicates you ways to enhance a collection of instruments for considering extra significantly approximately reasons. you will how one can query claims, determine motives, make judgements in keeping with causal info, and confirm explanations via additional checks. no matter if it really is understanding what information you wish, or figuring out that how you acquire and get ready info impacts the conclusions you could draw from it, Why can assist you sharpen your causal inference abilities.
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Additional info for Why: A Guide to Finding and Using Causes
If visitors to a particular site are strong supporters of a current president, then their results might say that the president’s approval ratings increase whenever she gives a major speech. Yet this shows only that there’s a correlation between approval and major speeches among people who are predisposed to liking the president (since that is the population responding to the survey). We’ll look at these and other forms of bias (like survivorship bias) in Chapter 7, as they affect what can be learned from experimental data.
While there’s evidence for many different components influencing judgments of blame, such as norms, intentions, and outcomes, the process by which these judgments are made is a topic of ongoing research. 44 Culture When studies say “90% of participants thought the driver caused the car accident,” who are those study participants? 45 This is not surprising since most research in this area takes place in universities, and participant pools made up of students make it possible to regularly recruit sufficient numbers of participants.
Block A is solid and B is patterned. The inferences made here do not fit completely with the associative model of causal learning, since the same associations can lead to different inferences. 25 The idea is that instead of using only pairwise associations, or the strength of connections between individual factors, people may learn about causes as part of a model that shows how many things are connected. A simple example is the structure in Figure 2-1b. This structure can also be augmented with the causes of insomnia (such as caffeine and stress) and the effects of late-night snacking (such as weight gain or cavities).
Why: A Guide to Finding and Using Causes by Samantha Kleinberg