
\subsection{Introduction}


\subsection{subgroup analysis}

\subsection{interaction terms}

\subsection{efficient policy learning}

\subsection{Het DML}
\(y=a(z)+d b(z)\)
Het effects is \(b(z)\)
We build groups instead of arbitrary function. So we estimate \(E[b(z)|G]\)

Use part of the data set to estimate

\(\hat y =\hat a (z)+D\hat b(z)\)

Use \(s=\hat b(z)\) to stratify. Key point is defining subgroups algorithmically. Less opportunity for hacking

\subsection{Continuous treatment effects}

\subsection{Intent-to-treat}

