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1/4
Since the word "estimand" seems to be giving readers problems, let me share my definition. First, it is indeed that which needs to be estimated, ..However, in the era of #causalinference, one needs to add something, to avoid confusion. #Bookofwhy
Since the word "estimand" seems to be giving readers problems, let me share my definition. First, it is indeed that which needs to be estimated, ..However, in the era of #causalinference, one needs to add something, to avoid confusion. #Bookofwhy
4/4
favorite text, or encyclopedia, or handbook. Why? Because those were written by statisticians prior to the causal era. For them, everything was properties of P, including the research question. The were not interested in causal questions which are not defined by P but by M.
favorite text, or encyclopedia, or handbook. Why? Because those were written by statisticians prior to the causal era. For them, everything was properties of P, including the research question. The were not interested in causal questions which are not defined by P but by M.
2/4
One needs to add that an "estimand" is a property of the distribution that governs the observed data,e.g. E[X], var[Y], E[Y|X=x],. We need it because causal quantities e.g., E[Y|do(x)] or E[Y_x|Z=z] may also need to be estimated, but they are not expressed as properties of P.
One needs to add that an "estimand" is a property of the distribution that governs the observed data,e.g. E[X], var[Y], E[Y|X=x],. We need it because causal quantities e.g., E[Y|do(x)] or E[Y_x|Z=z] may also need to be estimated, but they are not expressed as properties of P.
3/4
hence they are not "estimands" unless they are "identified", i.e., reduced to properties of P, and when they do, they can be regarded as "recipes" of what to do with our data (see #Bookofwhy) to get a valid answer to our causal question. This important distinction is not in
hence they are not "estimands" unless they are "identified", i.e., reduced to properties of P, and when they do, they can be regarded as "recipes" of what to do with our data (see #Bookofwhy) to get a valid answer to our causal question. This important distinction is not in
