\begin{algorithm}
\caption{Rejection Sampling algorithm to draw $y^{PS = S4}|x$}\label{alg:one}
\begin{algorithmic}
\State \textbf{Input:}  $x, \alpha, z, y$
\For{$i = 1 \text{ to } 8000$}
   \State Draw $u_i$  from Uniform(0,1)
    \State Draw $y^i_N$ from Normal(1.5, 4)
   \State $C \gets \frac{f(y^i_N|PS = S4,x,a,z)}{M*f_{Y_N}(y^i_N)}$
   \If{$u_i < C$}
    Accept $y^i_N$
\ElsIf{$u_i > C$}
    Reject $y^i_N$
\EndIf
\EndFor
\State \textbf{Output:} $y^{PS = S4}|x = $  the mean of the accepted $y^i_N$
\end{algorithmic}
\end{algorithm}

\begin{algorithm}
\caption{Rejection Sampling algorithm to draw $y^{PS = S4}|x$}\begin{algorithmic}
\State \textbf{Input:} $x, \alpha, z, y$
\For{$i = 1 \text{ to } 8000$}
\State Draw $u_i$ from Uniform(0,1)
\State Draw $y^i_N$ from Normal(1.5, 4)
\State $C \gets \frac{f(y^i_N|PS = S4,x,a,z)}{M*f_{Y_N}(y^i_N)}$
\If{$u_i < C$}
Accept $y^i_N$
\ElsIf{$u_i > C$}
Reject $y^i_N$
\EndIf
\EndFor
\State \textbf{Output:} $y^{PS = S4}|x = $ the mean of the accepted $y^i_N$
\end{algorithmic}
\end{algorithm}
{
    "title": "sensitivity analysis for constructing optimal regimes in the presence of   treatment non-compliance",
    "id": "2207.08964",
    "abstract": "the current body of research on developing optimal treatment strategies often places emphasis on intention-to-treat analyses, which fail to take into account the compliance behavior of individuals. methods based on instrumental variables have been developed to determine optimal treatment strategies in the presence of endogeneity. however, these existing methods are not applicable when there are two active treatment options and the average causal effects of the treatments cannot be identified using a binary instrument. in order to address this limitation, we present a procedure that can identify an optimal treatment strategy and the corresponding value function as a function of a vector of sensitivity parameters. additionally, we derive the canonical gradient of the target parameter and propose a multiply robust classification-based estimator for the optimal treatment strategy. through simulations, we demonstrate the practical need for and usefulness of our proposed method. we apply our method to a randomized trial on adaptive treatment for alcohol and cocaine dependence.",
    "categories": "stat.me",
    "doi": "",
    "created": "2022-07-18",
    "updated": "2024-02-20",
    "authors": [
        "cuong t. pham",
        "kevin g. lynch",
        "james r. mckay",
        "ashkan ertefaie"
    ],
    "affiliation": [],
    "url": "https://arxiv.org/abs/2207.08964"
}
{
    "ToxicityFilter": {
        "text_to_toxicity_attributes": {
            "imsart.sty": {
                "toxicity_score": 0.09525062,
                "severe_toxicity_score": 0.0038528442,
                "identity_attack_score": 0.0071035884,
                "insult_score": 0.02708937,
                "profanity_score": 0.0569429,
                "threat_score": 0.009204263,
                "sexually_explicit_score": 0.03774539,
                "flirtation_score": 0.3281287
            },
            "main-arxiv.tex": {
                "toxicity_score": 0.015142105,
                "severe_toxicity_score": 0.0012302399,
                "identity_attack_score": 0.0037552824,
                "insult_score": 0.008348881,
                "profanity_score": 0.01627459,
                "threat_score": 0.007832011,
                "sexually_explicit_score": 0.013210886,
                "flirtation_score": 0.23055424
            },
            "imsart.cls": {
                "toxicity_score": 0.014576632,
                "severe_toxicity_score": 0.0012397766,
                "identity_attack_score": 0.0034408006,
                "insult_score": 0.008234881,
                "profanity_score": 0.016479544,
                "threat_score": 0.007365964,
                "sexually_explicit_score": 0.0129749775,
                "flirtation_score": 0.33692676
            },
            "Binary-bib.bib": {
                "toxicity_score": 0.015707577,
                "severe_toxicity_score": 0.0012588501,
                "identity_attack_score": 0.0058826595,
                "insult_score": 0.008975885,
                "profanity_score": 0.01620627,
                "threat_score": 0.0077154995,
                "sexually_explicit_score": 0.0129749775,
                "flirtation_score": 0.45469284
            },
            "imsart-number.bst": {
                "toxicity_score": 0.019477395,
                "severe_toxicity_score": 0.0014019012,
                "identity_attack_score": 0.0037552824,
                "insult_score": 0.00979289,
                "profanity_score": 0.018119188,
                "threat_score": 0.00756015,
                "sexually_explicit_score": 0.01374168,
                "flirtation_score": 0.37246096
            },
            "imsart-nameyear.bst": {
                "toxicity_score": 0.0201057,
                "severe_toxicity_score": 0.0013446808,
                "identity_attack_score": 0.0036997858,
                "insult_score": 0.010172892,
                "profanity_score": 0.018119188,
                "threat_score": 0.007340072,
                "sexually_explicit_score": 0.011618502,
                "flirtation_score": 0.32274416
            },
            "main-arxiv.bbl": {
                "toxicity_score": 0.0126288915,
                "severe_toxicity_score": 0.0011491776,
                "identity_attack_score": 0.004069764,
                "insult_score": 0.007607877,
                "profanity_score": 0.014874061,
                "threat_score": 0.007624879,
                "sexually_explicit_score": 0.011618502,
                "flirtation_score": 0.20600256
            }
        },
        "reason": null
    }
}
{
    "num_done": {
        "figure": 0,
        "algorithm": 2,
        "plot": 0
    }
}
{
    "NonTrivialRenderingFilter": {
        "white_pixels_ratio": 92.94338603239127,
        "hash": "013f1f9f9f0f7f03",
        "most_frequent_color_ratio": 92.94338603239127
    }
}