\begin{algorithm}[h!]
\caption{Regression-Tree}\label{alg:rt}
\begin{algorithmic}
\State {\textbf{Input:}} $\{X_i,T_i,Y_i\}_{i=1}^n$, bandwidth $h$, tree depth $K$, number of features $L$
\If{K=1}
    \State Return $(\arg\max_p\sum_{i=1}^nK(\frac{T_i-p}{h})\frac{Y_i}{f(T_i\mid X_i)}, \max_p\sum_{i=1}^nK(\frac{T_i-p}{h})\frac{Y_i}{f(T_i\mid X_i)})$.
\Else
    \State Initialize Reward $=0$, tree = $\emptyset$
    \State Draw $l_1,\dots,l_L$ randomly from $d$ total features without replacement
    \For{$l = l_1,\dots,l_L$}
        \State Sort the data along $x_l$
        \For{$i=1,\dots,n$}
            \State (Tree\_left, Reward\_left) = \text{Regression-Tree}($\{X_j,T_j,Y_j\}_{j=1}^i,L-1$)
            \State (Tree\_right, Reward\_right) = \text{Regression-Tree}($\{X_j,T_j,Y_j\}_{j=i}^n,L-1$)
            \If{Reward\_left+Reward\_right$>$Reward}
                \State Reward = Reward\_left+Reward
                \State Tree = $[l,\frac{X_{l,i}+X_{l,i+1}}{2},\text{Tree\_left},\text{Tree\_right}]$
            \EndIf
        \EndFor 
    \EndFor 
    \State Return (Tree, Reward)
\EndIf
\end{algorithmic}
\end{algorithm}

\begin{algorithm}
[h!]
\caption{Regression-Tree}\begin{algorithmic}
\State {\textbf{Input:}} $\{X_i,T_i,Y_i\}_{i=1}^n$, bandwidth $h$, tree depth $K$, number of features $L$
\If{K=1}
\State Return $(\arg\max_p\sum_{i=1}^nK(\frac{T_i-p}{h})\frac{Y_i}{f(T_i\mid X_i)}, \max_p\sum_{i=1}^nK(\frac{T_i-p}{h})\frac{Y_i}{f(T_i\mid X_i)})$.
\Else
\State Initialize Reward $=0$, tree = $\emptyset$
\State Draw $l_1,\dots,l_L$ randomly from $d$ total features without replacement
\For{$l = l_1,\dots,l_L$}
\State Sort the data along $x_l$
\For{$i=1,\dots,n$}
\State (Tree\_left, Reward\_left) = \text{Regression-Tree}($\{X_j,T_j,Y_j\}_{j=1}^i,L-1$)
\State (Tree\_right, Reward\_right) = \text{Regression-Tree}($\{X_j,T_j,Y_j\}_{j=i}^n,L-1$)
\If{Reward\_left+Reward\_right$>$Reward}
\State Reward = Reward\_left+Reward
\State Tree = $[l,\frac{X_{l,i}+X_{l,i+1}}{2},\text{Tree\_left},\text{Tree\_right}]$
\EndIf
\EndFor
\EndFor
\State Return (Tree, Reward)
\EndIf
\end{algorithmic}
\end{algorithm}
{
    "title": "data-driven policy learning for a continuous treatment",
    "id": "2402.02535",
    "abstract": "this paper studies policy learning under the condition of unconfoundedness with a continuous treatment variable. our research begins by employing kernel-based inverse propensity-weighted (ipw) methods to estimate policy welfare. we aim to approximate the optimal policy within a global policy class characterized by infinite vapnik-chervonenkis (vc) dimension. this is achieved through the utilization of a sequence of sieve policy classes, each with finite vc dimension. preliminary analysis reveals that welfare regret comprises of three components: global welfare deficiency, variance, and bias. this leads to the necessity of simultaneously selecting the optimal bandwidth for estimation and the optimal policy class for welfare approximation. to tackle this challenge, we introduce a semi-data-driven strategy that employs penalization techniques. this approach yields oracle inequalities that adeptly balance the three components of welfare regret without prior knowledge of the welfare deficiency. by utilizing precise maximal and concentration inequalities, we derive sharper regret bounds than those currently available in the literature. in instances where the propensity score is unknown, we adopt the doubly robust (dr) moment condition tailored to the continuous treatment setting. in alignment with the binary-treatment case, the dr welfare regret closely parallels the ipw welfare regret, given the fast convergence of nuisance estimators.",
    "categories": "econ.em",
    "doi": "",
    "created": "2024-02-04",
    "updated": "",
    "authors": [
        "chunrong ai",
        "yue fang",
        "haitian xie"
    ],
    "affiliation": [],
    "url": "https://arxiv.org/abs/2402.02535"
}
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