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I've already read this: Conditional expectation function

For this question, we assume familiar notation in linear regression, with $Y$ being the response and stochastic regressors $X$. I've seen both $E(Y|X)$ and $E(Y|X=x)$ referred to as the "conditional expectation function".

I understand that $E(Y|X)$ is a random variable while $E(Y|X=x)$ is a realization of $E(Y|X=x)$. That being said, in the regression setup, we view realizations of Y and X, which we denote $(y, x)$.

Therefore, we condition on the fact that $X=x$. Then, we use our data to estimate $E(Y|X=x)$.

My question is when $E(Y|X)$ comes into play at all? Where do we even consider it? Or is it irrelevant, since we've already observed a realization of it?

A great answer would also explain why we call both $E(Y|X)$ and $E(Y|X=x)$ the conditional regression functions, as they seem to be certainly related, yet different objects; one is random while the other is deterministic.

Notice that throughout my answer, we've assumed $X$ is stochastic. Therefore, the question is not a question about stochastic vs fixed regressors.

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    $\begingroup$ Is this question about notation (the two are meaning the same) or about understanding the concept? $\endgroup$ Dec 15, 2020 at 8:09
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    $\begingroup$ I remember recently seeing a question about this different notation. The answer contained something like P(Y|X) being more used for events or categorical variables like P(sick | runny nose) and the notation P(Y|X=x) is used to stress that X is a numerical variable like P(sick | body temperature) $\endgroup$ Dec 15, 2020 at 8:09
  • $\begingroup$ I was asking both. If you could share the question you saw in addition to answering the question that would be great too! $\endgroup$
    – user303375
    Dec 15, 2020 at 8:14
  • $\begingroup$ In response to the second comment, the distinction is certainly not between categorical and numerical, at least where I've seen it used. Particularly in this link: timlrx.com/2018/02/26/… $\endgroup$
    – user303375
    Dec 15, 2020 at 8:18
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    $\begingroup$ That link is very nice +1 for Dilip. I do not disagree so much with the explanation there. Indeed $E(Y|X)$ can be a random variable $E(Y|X=x)$ is not. However, I do believe that you can see it more broad. The notation $X=x$ versus $X$ is not just about discriminating between random variables and specific outcomes. $\endgroup$ Dec 15, 2020 at 17:34

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In linear regression, we assume the random variable $E(Y|X)$ has the parametric form $X\beta$. When we "fit" the model, we are estimating the parameter $\beta$, which according to our assumption gives us a general formula for $E(Y|X)$. If we made no assumptions whatsoever about the form of $E(Y|X)$, then it would be true that we would only be able to estimate $E(Y|X=x)$ for our observed realizations $(x,y)$. But that wouldn't be a linear regression model.

The fact that one might refer to both $E(Y|X)$ and $E(Y|X=x)$ by the name "conditional regression function" is just a case of overloading terminology. The first can be thought of as a function of the random variable X, the second can be thought of as a function of the fixed sample $x$. This conflation happens a lot with random variables--one might refer to "the sum of a two rolled dice", which can be interpreted both as a function of random variables (the dice themselves being random), or a function of the fixed outcome of two rolled dice, which is deterministic.

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  • $\begingroup$ Here's my understanding: $E(Y|X)$ appears in the context when $X$ has yet to be observed; i.e. Y = X\beta + \epsilon. In a sense, this is the "unconditional" relationship between $Y$, $X$, and $\epsilon$, as we have not observed $X$ quite yet, so $X$ in addition to $\epsilon$ are still random. $E(Y|X=x)$ appears when $X$ has already been observed as $x$. Therefore, this gives the "conditional on X=x" relationship $Y = x\beta + \epsilon$, where only $\epsilon$ is random. Is this what you were getting at? $\endgroup$
    – user303375
    Dec 15, 2020 at 18:33
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    $\begingroup$ No, I would not refer to $E(Y|X)$ as "unconditional". They both reflect a statistic of $Y$ conditioned on $X$. They differ only in whether we are speaking of this relationship generally, or at one specific value. The unconditional expectation $E(Y)$ is likely completely different from $E(Y|X)$. EDIT: To clarify, $Y$ is a random variable with a distribution. It also has a conditional distribution $Y|X$, which is different unless $Y$ and $X$ are independent. We are interested in calculating a statistic of this conditional distribution. $\endgroup$ Dec 15, 2020 at 18:42
  • $\begingroup$ Ah makes more sense then. Let me rephrase. If we have not yet observed $X$ yet, then, $Y = X\beta + \epsilon = E(Y|X) + \epsilon$ where $X$ and $\epsilon$ are random. Once we observe the data, $Y = x\beta + \epsilon = E(Y|X=x) + \epsilon$, where only $\epsilon$ is random. We mainly operate in the second case because we're in the specific situation where $X=x$. It is only because we are assuming that $E(Y|X)$ and $E(Y|X=x)$ are linear in $\beta$ that we are able to construct estimates for $\beta$, and therefore estimate both functions. I think this is correct? $\endgroup$
    – user303375
    Dec 15, 2020 at 18:57

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