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<h1>All modules for which code is available</h1>
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<ul><li><a href="med_bench/estimation/mediation_coefficient_product.html">med_bench.estimation.mediation_coefficient_product</a></li>
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<h1>Source code for med_bench.estimation.mediation_coefficient_product</h1><div class="highlight"><pre>
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<span></span><span class="kn">import</span><span class="w"> </span><span class="nn">numpy</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">np</span>
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<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.linear_model</span><span class="w"> </span><span class="kn">import</span> <span class="n">RidgeCV</span>
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<span class="kn">from</span><span class="w"> </span><span class="nn">med_bench.estimation.base</span><span class="w"> </span><span class="kn">import</span> <span class="n">Estimator</span>
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<span class="kn">from</span><span class="w"> </span><span class="nn">med_bench.utils.constants</span><span class="w"> </span><span class="kn">import</span> <span class="n">ALPHAS</span><span class="p">,</span> <span class="n">CV_FOLDS</span><span class="p">,</span> <span class="n">TINY</span>
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<span class="kn">from</span><span class="w"> </span><span class="nn">med_bench.utils.decorators</span><span class="w"> </span><span class="kn">import</span> <span class="n">fitted</span>
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<div class="viewcode-block" id="CoefficientProduct">
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<a class="viewcode-back" href="../../../modules.html#med_bench.estimation.mediation_coefficient_product.CoefficientProduct">[docs]</a>
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<span class="k">class</span><span class="w"> </span><span class="nc">CoefficientProduct</span><span class="p">(</span><span class="n">Estimator</span><span class="p">):</span>
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<span class="w"> </span><span class="sd">&quot;&quot;&quot;Coefficient Product estimatation method class&quot;&quot;&quot;</span>
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<span class="k">def</span><span class="w"> </span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">regularize</span><span class="p">:</span> <span class="nb">bool</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
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<span class="w"> </span><span class="sd">&quot;&quot;&quot;Initializes Coefficient product estimatation method</span>
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<span class="sd"> Parameters</span>
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<span class="sd"> ----------</span>
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<span class="sd"> regularize (bool) : regularization parameter</span>
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<span class="sd"> &quot;&quot;&quot;</span>
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<span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
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<span class="bp">self</span><span class="o">.</span><span class="n">_regularize</span> <span class="o">=</span> <span class="n">regularize</span>
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<div class="viewcode-block" id="CoefficientProduct.fit">
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<a class="viewcode-back" href="../../../modules.html#med_bench.estimation.mediation_coefficient_product.CoefficientProduct.fit">[docs]</a>
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<span class="k">def</span><span class="w"> </span><span class="nf">fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">t</span><span class="p">,</span> <span class="n">m</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
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<span class="w"> </span><span class="sd">&quot;&quot;&quot;Fits nuisance parameters to data</span>
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<span class="sd"> Parameters</span>
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<span class="sd"> ----------</span>
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<span class="sd"> t array-like, shape (n_samples)</span>
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<span class="sd"> treatment value for each unit, binary</span>
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<span class="sd"> m array-like, shape (n_samples)</span>
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<span class="sd"> mediator value for each unit, here m is necessary binary and uni-</span>
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<span class="sd"> dimensional</span>
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<span class="sd"> x array-like, shape (n_samples, n_features_covariates)</span>
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<span class="sd"> covariates (potential confounders) values</span>
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<span class="sd"> y array-like, shape (n_samples)</span>
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<span class="sd"> outcome value for each unit, continuous</span>
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<span class="sd"> &quot;&quot;&quot;</span>
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<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_regularize</span><span class="p">:</span>
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<span class="n">alphas</span> <span class="o">=</span> <span class="n">ALPHAS</span>
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<span class="k">else</span><span class="p">:</span>
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<span class="n">alphas</span> <span class="o">=</span> <span class="p">[</span><span class="n">TINY</span><span class="p">]</span>
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<span class="n">t</span><span class="p">,</span> <span class="n">m</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_resize</span><span class="p">(</span><span class="n">t</span><span class="p">,</span> <span class="n">m</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
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<span class="bp">self</span><span class="o">.</span><span class="n">_coef_t_m</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">m</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
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<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">m</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]):</span>
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<span class="n">m_reg</span> <span class="o">=</span> <span class="n">RidgeCV</span><span class="p">(</span><span class="n">alphas</span><span class="o">=</span><span class="n">alphas</span><span class="p">,</span> <span class="n">cv</span><span class="o">=</span><span class="n">CV_FOLDS</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span>
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<span class="n">np</span><span class="o">.</span><span class="n">hstack</span><span class="p">((</span><span class="n">x</span><span class="p">,</span> <span class="n">t</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">))),</span> <span class="n">m</span><span class="p">[:,</span> <span class="n">i</span><span class="p">]</span>
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<span class="p">)</span>
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<span class="bp">self</span><span class="o">.</span><span class="n">_coef_t_m</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="n">m_reg</span><span class="o">.</span><span class="n">coef_</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
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<span class="n">y_reg</span> <span class="o">=</span> <span class="n">RidgeCV</span><span class="p">(</span><span class="n">alphas</span><span class="o">=</span><span class="n">alphas</span><span class="p">,</span> <span class="n">cv</span><span class="o">=</span><span class="n">CV_FOLDS</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span>
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<span class="n">np</span><span class="o">.</span><span class="n">hstack</span><span class="p">((</span><span class="n">x</span><span class="p">,</span> <span class="n">t</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span> <span class="n">m</span><span class="p">)),</span> <span class="n">y</span>
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<span class="p">)</span>
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<span class="bp">self</span><span class="o">.</span><span class="n">_coef_y</span> <span class="o">=</span> <span class="n">y_reg</span><span class="o">.</span><span class="n">coef_</span>
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<span class="bp">self</span><span class="o">.</span><span class="n">_fitted</span> <span class="o">=</span> <span class="kc">True</span>
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<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbose</span><span class="p">:</span>
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<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Nuisance models fitted&quot;</span><span class="p">)</span></div>
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<span class="nd">@fitted</span>
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<span class="k">def</span><span class="w"> </span><span class="nf">estimate</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">t</span><span class="p">,</span> <span class="n">m</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
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<span class="w"> </span><span class="sd">&quot;&quot;&quot;Estimates causal effect on data&quot;&quot;&quot;</span>
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<span class="n">direct_effect_treated</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_coef_y</span><span class="p">[</span><span class="n">x</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]]</span>
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<span class="n">direct_effect_control</span> <span class="o">=</span> <span class="n">direct_effect_treated</span>
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<span class="n">indirect_effect_treated</span> <span class="o">=</span> <span class="nb">sum</span><span class="p">(</span>
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<span class="bp">self</span><span class="o">.</span><span class="n">_coef_y</span><span class="p">[</span><span class="n">x</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">+</span> <span class="mi">1</span> <span class="p">:]</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">_coef_t_m</span>
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<span class="p">)</span>
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<span class="n">indirect_effect_control</span> <span class="o">=</span> <span class="n">indirect_effect_treated</span>
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<span class="n">causal_effects</span> <span class="o">=</span> <span class="p">{</span>
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<span class="s2">&quot;total_effect&quot;</span><span class="p">:</span> <span class="n">direct_effect_treated</span> <span class="o">+</span> <span class="n">indirect_effect_control</span><span class="p">,</span>
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<span class="s2">&quot;direct_effect_treated&quot;</span><span class="p">:</span> <span class="n">direct_effect_treated</span><span class="p">,</span>
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<span class="s2">&quot;direct_effect_control&quot;</span><span class="p">:</span> <span class="n">direct_effect_control</span><span class="p">,</span>
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<span class="s2">&quot;indirect_effect_treated&quot;</span><span class="p">:</span> <span class="n">indirect_effect_treated</span><span class="p">,</span>
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<span class="s2">&quot;indirect_effect_control&quot;</span><span class="p">:</span> <span class="n">indirect_effect_control</span><span class="p">,</span>
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<span class="p">}</span>
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<span class="k">return</span> <span class="n">causal_effects</span></div>
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</pre></div>
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