Metadata-Version: 2.1
Name: opossum
Version: 0.0.2
Summary: Simulated Data Generating Process
Home-page: https://humboldt-wi.github.io/blog/research/
Author: Tobias Krebs, Julian Winkel
Author-email: julian.winkel@hu-berlin.de
License: UNKNOWN
Description: 
        # Simulated Data Generating Process
        
        ## Task: Simulate realistic data set with known treatment effects
        
        ###### Properties of the data generator: 
        
        - High dimensional and non-linear data
        - flexible
        - user friendly
        
        ## Motivation
        
        - Provide high quality data for research 
        
        - Useful for you to test your models
        
        ## Model Equations
        
        <br/>
        <a href="https://www.codecogs.com/eqnedit.php?latex=$$&space;\\&space;Y&space;=&space;\theta_0&space;D&space;&plus;&space;g_0(X)&plus;&space;U,&space;&&&space;&&space;E[U|X,D]&space;\\&space;D&space;=&space;m_0(X)&space;&plus;&space;V,&space;&&&space;&&space;E[V|X]&space;=&space;0&space;\\&space;\theta_0&space;=&space;t_0(Z)&space;&plus;&space;W,&space;&&&space;&&space;E[W|Z]&space;=&space;0,&space;Z&space;\subseteq&space;X&space;\\&space;$$" target="_blank"><img src="https://latex.codecogs.com/gif.latex?$$&space;\\&space;Y&space;=&space;\theta_0&space;D&space;&plus;&space;g_0(X)&plus;&space;U,&space;&&&space;&&space;E[U|X,D]&space;\\&space;D&space;=&space;m_0(X)&space;&plus;&space;V,&space;&&&space;&&space;E[V|X]&space;=&space;0&space;\\&space;\theta_0&space;=&space;t_0(Z)&space;&plus;&space;W,&space;&&&space;&&space;E[W|Z]&space;=&space;0,&space;Z&space;\subseteq&space;X&space;\\&space;$$" title="$$ \\ Y = \theta_0 D + g_0(X)+ U, && & E[U|X,D] \\ D = m_0(X) + V, && & E[V|X] = 0 \\ \theta_0 = t_0(Z) + W, && & E[W|Z] = 0, Z \subseteq X \\ $$" /></a>
           
        Y - Outcome Variable
        
        <a href="https://www.codecogs.com/eqnedit.php?latex=$\theta_0$" target="_blank"><img src="https://latex.codecogs.com/gif.latex?$\theta_0$" title="$\theta_0$" /></a> - True treatment effect 
        
        D - Treatment Dummy
        
        <a href="https://www.codecogs.com/eqnedit.php?latex=$X_{n*k}$" target="_blank"><img src="https://latex.codecogs.com/gif.latex?$X_{n*k}$" title="$X_{n*k}$" /></a> - Covariates
        
        ## Treatment Assignment
        ##### Random
        Choose probability <a href="https://www.codecogs.com/eqnedit.php?latex=$m_0$" target="_blank"><img src="https://latex.codecogs.com/gif.latex?$m_0$" title="$m_0$" /></a>
        
        ##### Conditioned covariates 
        Weight vector <a href="https://www.codecogs.com/eqnedit.php?latex=$\;&space;b_{k*1}&space;\stackrel{ind.}{\sim}&space;U(0,1),&space;\qquad&space;a_{n*1}&space;=&space;X_{n*k}&space;*&space;b_{k*1}$" target="_blank"><img src="https://latex.codecogs.com/gif.latex?$\;&space;b_{k*1}&space;\stackrel{ind.}{\sim}&space;U(0,1),&space;\qquad&space;a_{n*1}&space;=&space;X_{n*k}&space;*&space;b_{k*1}$" title="$\; b_{k*1} \stackrel{ind.}{\sim} U(0,1), \qquad a_{n*1} = X_{n*k} * b_{k*1}$" /></a>
        
        <a href="https://www.codecogs.com/eqnedit.php?latex=$$m_0(X)&space;=&space;\Phi\left(\frac{a-\hat{\mu}(a)}{\hat{\sigma}(a)}\right)&space;$$" target="_blank"><img src="https://latex.codecogs.com/gif.latex?$$m_0(X)&space;=&space;\Phi\left(\frac{a-\hat{\mu}(a)}{\hat{\sigma}(a)}\right)&space;$$" title="$$m_0(X) = \Phi\left(\frac{a-\hat{\mu}(a)}{\hat{\sigma}(a)}\right) $$" /></a>
        
        ##### Create assignment vector
        <a href="https://www.codecogs.com/eqnedit.php?latex=$D&space;\stackrel{ind.}{\sim}&space;Bernoulli(m_0)$" target="_blank"><img src="https://latex.codecogs.com/gif.latex?$D&space;\stackrel{ind.}{\sim}&space;Bernoulli(m_0)$" title="$D \stackrel{ind.}{\sim} Bernoulli(m_0)$" /></a>
        
        
        ## Treatment effect
        
        ###### Options 1/2: Constant, positive and negative
        <a href="https://www.codecogs.com/eqnedit.php?latex=$&space;\theta_0&space;=&space;intensity&space;*&space;0.1&space;\newline&space;\theta_0&space;=&space;-intensity&space;*&space;0.1&space;$" target="_blank"><img src="https://latex.codecogs.com/gif.latex?$&space;\theta_0&space;=&space;intensity&space;*&space;0.1&space;\newline&space;\theta_0&space;=&space;-intensity&space;*&space;0.1&space;$" title="$ \theta_0 = intensity * 0.1 \newline \theta_0 = -intensity * 0.1 $" /></a>
        
        ###### Options 3/4: Continuous heterogeneous effect, positive and negative
        
        <a href="https://www.codecogs.com/eqnedit.php?latex=$Z&space;\subseteq&space;X,\quad&space;Weight&space;vector&space;\quad&space;b_{k*1}&space;\stackrel{ind.}{\sim}&space;U(0,1),&space;\quad&space;W&space;\stackrel{ind.}{\sim}&space;N(0,1)$" target="_blank"><img src="https://latex.codecogs.com/gif.latex?$Z&space;\subseteq&space;X,\quad&space;Weight&space;vector&space;\quad&space;b_{k*1}&space;\stackrel{ind.}{\sim}&space;U(0,1),&space;\quad&space;W&space;\stackrel{ind.}{\sim}&space;N(0,1)$" title="$Z \subseteq X,\quad Weight vector \quad b_{k*1} \stackrel{ind.}{\sim} U(0,1), \quad W \stackrel{ind.}{\sim} N(0,1)$" /></a>
        
        <a href="https://www.codecogs.com/eqnedit.php?latex=$$\gamma&space;=&space;sin(Z&space;*&space;b)^2&space;&plus;&space;W$$" target="_blank"><img src="https://latex.codecogs.com/gif.latex?$$\gamma&space;=&space;sin(Z&space;*&space;b)^2&space;&plus;&space;W$$" title="$$\gamma = sin(Z * b)^2 + W$$" /></a>
        
        <a href="https://www.codecogs.com/eqnedit.php?latex=$$\theta_0&space;=&space;\frac{\gamma&space;-&space;min(\gamma)}{max(\gamma)&space;-&space;min(\gamma)}(intensity&space;*&space;0.2)$$" target="_blank"><img src="https://latex.codecogs.com/gif.latex?$$\theta_0&space;=&space;\frac{\gamma&space;-&space;min(\gamma)}{max(\gamma)&space;-&space;min(\gamma)}(intensity&space;*&space;0.2)$$" title="$$\theta_0 = \frac{\gamma - min(\gamma)}{max(\gamma) - min(\gamma)}(intensity * 0.2)$$" /></a>
        
        ###### Option 5: No treatment effect
        <a href="https://www.codecogs.com/eqnedit.php?latex=$\theta_0&space;=&space;0$" target="_blank"><img src="https://latex.codecogs.com/gif.latex?$\theta_0&space;=&space;0$" title="$\theta_0 = 0$" /></a>
        
        ## Composition of dependent variable 
        
        ###### Non-linearity: 
        <a href="https://www.codecogs.com/eqnedit.php?latex=$$g(X)&space;=&space;sin(X*b)^2&space;&plus;&space;U$$" target="_blank"><img src="https://latex.codecogs.com/gif.latex?$$g(X)&space;=&space;sin(X*b)^2&space;&plus;&space;U$$" title="$$g(X) = sin(X*b)^2 + U$$" /></a>
        
        ###### Option 1: Continuous 
        <a href="https://www.codecogs.com/eqnedit.php?latex=$$Y_i&space;=&space;\theta_{0,i}&space;D_i&space;&plus;&space;g_0(X_i)&plus;&space;U_i$$" target="_blank"><img src="https://latex.codecogs.com/gif.latex?$$Y_i&space;=&space;\theta_{0,i}&space;D_i&space;&plus;&space;g_0(X_i)&plus;&space;U_i$$" title="$$Y_i = \theta_{0,i} D_i + g_0(X_i)+ U_i$$" /></a>
        
        ###### Option 2: Binary
        <a href="https://www.codecogs.com/eqnedit.php?latex=$$p_i&space;=&space;\frac{Y_i&space;-&space;min(Y_i)}{max(Y_i)&space;-&space;min(Y_i)}(0.9&space;-&space;0.1)$$" target="_blank"><img src="https://latex.codecogs.com/gif.latex?$$p_i&space;=&space;\frac{Y_i&space;-&space;min(Y_i)}{max(Y_i)&space;-&space;min(Y_i)}(0.9&space;-&space;0.1)$$" title="$$p_i = \frac{Y_i - min(Y_i)}{max(Y_i) - min(Y_i)}(0.9 - 0.1)$$" /></a>
        <br/>
        <a href="https://www.codecogs.com/eqnedit.php?latex=$$\theta_0&space;\stackrel{ind.}{\sim}&space;Bernoulli(p_i)$$" target="_blank"><img src="https://latex.codecogs.com/gif.latex?$$\theta_0&space;\stackrel{ind.}{\sim}&space;Bernoulli(p_i)$$" title="$$\theta_0 \stackrel{ind.}{\sim} Bernoulli(p_i)$$" /></a>
        
        ## Application of module/package 
        
        
        ```python
        from opossum import UserInterface
        
        u = UserInterface(N = 10000,k = 10, seed = 12)
        
        u.generate_treatment(random_assignment = True,
                             assignment_prob = 0.5,
                             constant_pos = True,
                             constant_neg = False,
                             heterogeneous_pos = False,
                             heterogeneous_neg = False,
                             no_treatment = False,
                             treatment_option_weights = [0, 0, 0.7, 0.1, 0.2], # treatment_option_weights default: None 
                             # [constant_pos,constant_neg, heterogeneous_pos, heterogeneous_neg, no effect]
                             intensity = 5)
        
        y, X, assignment_vector, treatment_effect = u.output_data(binary=False)
             
        
        ```
        
        ## Correlation Matrix of X
        
        
        ```python
        %matplotlib notebook
        u.plot_covariates_correlation()
        ```
        
        
            <IPython.core.display.Javascript object>
        
        
        
        <img 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" width="640">
        
        
        ## Distribution of propensity scores according to treatment assignment
        
        <img src=plots/propensity_scores.png>
        
        ## Treatment effect options 
        
        <img src=plots/treatment_effect_options.png>
        
        ## Customized treatment distribution
        
        <img src=plots/realistic_treatment_effect.png>
        
        ## Outputs depending on treatment assignment 
        
        <img src=plots/continuous_output.png>
        
        <img src=plots/binary_output.png>
        
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Description-Content-Type: text/markdown
