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Causal Inference The Mixtape - 2 Probability and Regression Review

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In practice, causal inference is based on statistical models that range from the very simple to extremely advanced. And building such models requires some rudimentary knowledge of probability theory, so let’s begin with some definitions. A random process is a process that can be repeated many times with different outcomes each time. The sample space is the set of all the possible outcomes of a random process. We distinguish between discrete and continuous random processes Table 1 below. Discrete processes produce, integers, whereas continuous processes produce fractions as well. We define independent events two ways. The first refers to logical independence. For instance, two events occur but there is no reason to believe that the two events affect each other. When it is assumed that they do affect each other, this is a logical fallacy called post hoc ergo propter hoc, which is Latin for “after this, therefore because of this.” This fallacy recognizes that the temporal ordering of even

2 Probability and Regression Review – Causal Inference The Mixtape Causal Inference: The Mixtape. Buy the print version today: Buy from Amazon Buy from Yale Press \[ % Define terms \newcommand{\Card}{\text{Card }} \DeclareMathOperator*{\cov}{cov} \DeclareMathOperator*{\var}{var} \DeclareMathOperator{\Var}{Var\,} \DeclareMathOperator{\Cov}{Cov\,} \DeclareMathOperator{\Prob}{Prob} \DeclareMathOperator{\Pr}{Pr} \newcommand{\independent}{\perp \!\!\! \perp} \DeclareMathOperator{\Post}{Post} \DeclareMathOperator{\Pre}{Pre} \De

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Causal Inference The Mixtape - 2 Probability and Regression Review

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