minor edit

README.md

@@ -12,7 +12,7 @@
 - Sensitivity analysis to [assess robustness of causal estimates](https://amaiya.github.io/causalnlp/causalinference.html#CausalInferenceModel.evaluate_robustness)
 - Quick and simple [key driver analysis](https://amaiya.github.io/causalnlp/key_driver_analysis.html) to yield clues on potential drivers of an outcome based on predictive power, correlations, etc.
 - Can easily be applied to ["traditional" tabular datasets without text](https://amaiya.github.io/causalnlp/examples.html#What-is-the-causal-impact-of-having-a-PhD-on-making-over-$50K?) (i.e., datasets with only numerical and categorical variables)
-- Includes an experimental PyTorch implementation of [CausalBert](https://arxiv.org/abs/1905.12741) by Veitch, Sridar, and Blei (based on [reference implementation](https://github.com/rpryzant/causal-bert-pytorch) by R. Pryzant.)
+- Includes an experimental PyTorch implementation of [CausalBert](https://arxiv.org/abs/1905.12741) by Veitch, Sridar, and Blei (based on [reference implementation](https://github.com/rpryzant/causal-bert-pytorch) by R. Pryzant)
 
 ## Install
 

docs/index.html

@@ -46,7 +46,7 @@ <h2 id="Features">Features<a class="anchor-link" href="#Features"> </a></h2><ul>
 <li>Sensitivity analysis to <a href="https://amaiya.github.io/causalnlp/causalinference.html#CausalInferenceModel.evaluate_robustness">assess robustness of causal estimates</a></li>
 <li>Quick and simple <a href="https://amaiya.github.io/causalnlp/key_driver_analysis.html">key driver analysis</a> to yield clues on potential drivers of an outcome based on predictive power, correlations, etc.</li>
 <li>Can easily be applied to <a href="https://amaiya.github.io/causalnlp/examples.html#What-is-the-causal-impact-of-having-a-PhD-on-making-over-$50K?">"traditional" tabular datasets without text</a> (i.e., datasets with only numerical and categorical variables)</li>
-<li>Includes an experimental PyTorch implementation of <a href="https://arxiv.org/abs/1905.12741">CausalBert</a> by Veitch, Sridar, and Blei (based on <a href="https://github.com/rpryzant/causal-bert-pytorch">reference implementation</a> by R. Pryzant.)</li>
+<li>Includes an experimental PyTorch implementation of <a href="https://arxiv.org/abs/1905.12741">CausalBert</a> by Veitch, Sridar, and Blei (based on <a href="https://github.com/rpryzant/causal-bert-pytorch">reference implementation</a> by R. Pryzant)</li>
 </ul>
 
 </div>

docs/meta.utils.html

@@ -356,7 +356,7 @@ <h4 id="gini" class="doc_header"><code>gini</code><a href="https://github.com/am
 
 
 <div class="output_markdown rendered_html output_subarea ">
-<h4 id="regression_metrics" class="doc_header"><code>regression_metrics</code><a href="https://github.com/amaiya/causalnlp/tree/main/causalnlp/meta/utils.py#L235" class="source_link" style="float:right">[source]</a></h4><blockquote><p><code>regression_metrics</code>(<strong><code>y</code></strong>, <strong><code>p</code></strong>, <strong><code>w</code></strong>=<em><code>None</code></em>, <strong><code>metrics</code></strong>=<em><code>{'RMSE': &lt;function rmse at 0x7f84480da048&gt;, 'sMAPE': &lt;function smape at 0x7f8448c73f28&gt;, 'Gini': &lt;function gini at 0x7f84480da0d0&gt;}</code></em>)</p>
+<h4 id="regression_metrics" class="doc_header"><code>regression_metrics</code><a href="https://github.com/amaiya/causalnlp/tree/main/causalnlp/meta/utils.py#L235" class="source_link" style="float:right">[source]</a></h4><blockquote><p><code>regression_metrics</code>(<strong><code>y</code></strong>, <strong><code>p</code></strong>, <strong><code>w</code></strong>=<em><code>None</code></em>, <strong><code>metrics</code></strong>=<em><code>{'RMSE': &lt;function rmse at 0x7f239bca5048&gt;, 'sMAPE': &lt;function smape at 0x7f239c83ef28&gt;, 'Gini': &lt;function gini at 0x7f239bca50d0&gt;}</code></em>)</p>
 </blockquote>
 <p>Log metrics for regressors.</p>
 <p>Args:
@@ -424,7 +424,7 @@ <h4 id="logloss" class="doc_header"><code>logloss</code><a href="https://github.
 
 
 <div class="output_markdown rendered_html output_subarea ">
-<h4 id="classification_metrics" class="doc_header"><code>classification_metrics</code><a href="https://github.com/amaiya/causalnlp/tree/main/causalnlp/meta/utils.py#L279" class="source_link" style="float:right">[source]</a></h4><blockquote><p><code>classification_metrics</code>(<strong><code>y</code></strong>, <strong><code>p</code></strong>, <strong><code>w</code></strong>=<em><code>None</code></em>, <strong><code>metrics</code></strong>=<em><code>{'AUC': &lt;function roc_auc_score at 0x7f8480f7a048&gt;, 'Log Loss': &lt;function logloss at 0x7f84480da1e0&gt;}</code></em>)</p>
+<h4 id="classification_metrics" class="doc_header"><code>classification_metrics</code><a href="https://github.com/amaiya/causalnlp/tree/main/causalnlp/meta/utils.py#L279" class="source_link" style="float:right">[source]</a></h4><blockquote><p><code>classification_metrics</code>(<strong><code>y</code></strong>, <strong><code>p</code></strong>, <strong><code>w</code></strong>=<em><code>None</code></em>, <strong><code>metrics</code></strong>=<em><code>{'AUC': &lt;function roc_auc_score at 0x7f23d4b45048&gt;, 'Log Loss': &lt;function logloss at 0x7f239bca51e0&gt;}</code></em>)</p>
 </blockquote>
 <p>Log metrics for classifiers.</p>
 <p>Args:
@@ -566,7 +566,7 @@ <h2 id="NearestNeighborMatch" class="doc_header"><code>class</code> <code>Neares
 
 
 <div class="output_markdown rendered_html output_subarea ">
-<h2 id="MatchOptimizer" class="doc_header"><code>class</code> <code>MatchOptimizer</code><a href="https://github.com/amaiya/causalnlp/tree/main/causalnlp/meta/utils.py#L501" class="source_link" style="float:right">[source]</a></h2><blockquote><p><code>MatchOptimizer</code>(<strong><code>treatment_col</code></strong>=<em><code>'is_treatment'</code></em>, <strong><code>ps_col</code></strong>=<em><code>'pihat'</code></em>, <strong><code>user_col</code></strong>=<em><code>None</code></em>, <strong><code>matching_covariates</code></strong>=<em><code>['pihat']</code></em>, <strong><code>max_smd</code></strong>=<em><code>0.1</code></em>, <strong><code>max_deviation</code></strong>=<em><code>0.1</code></em>, <strong><code>caliper_range</code></strong>=<em><code>(0.01, 0.5)</code></em>, <strong><code>max_pihat_range</code></strong>=<em><code>(0.95, 0.999)</code></em>, <strong><code>max_iter_per_param</code></strong>=<em><code>5</code></em>, <strong><code>min_users_per_group</code></strong>=<em><code>1000</code></em>, <strong><code>smd_cols</code></strong>=<em><code>['pihat']</code></em>, <strong><code>dev_cols_transformations</code></strong>=<em><code>{'pihat': &lt;function mean at 0x7f85f41400d0&gt;}</code></em>, <strong><code>dev_factor</code></strong>=<em><code>1.0</code></em>, <strong><code>verbose</code></strong>=<em><code>True</code></em>)</p>
+<h2 id="MatchOptimizer" class="doc_header"><code>class</code> <code>MatchOptimizer</code><a href="https://github.com/amaiya/causalnlp/tree/main/causalnlp/meta/utils.py#L501" class="source_link" style="float:right">[source]</a></h2><blockquote><p><code>MatchOptimizer</code>(<strong><code>treatment_col</code></strong>=<em><code>'is_treatment'</code></em>, <strong><code>ps_col</code></strong>=<em><code>'pihat'</code></em>, <strong><code>user_col</code></strong>=<em><code>None</code></em>, <strong><code>matching_covariates</code></strong>=<em><code>['pihat']</code></em>, <strong><code>max_smd</code></strong>=<em><code>0.1</code></em>, <strong><code>max_deviation</code></strong>=<em><code>0.1</code></em>, <strong><code>caliper_range</code></strong>=<em><code>(0.01, 0.5)</code></em>, <strong><code>max_pihat_range</code></strong>=<em><code>(0.95, 0.999)</code></em>, <strong><code>max_iter_per_param</code></strong>=<em><code>5</code></em>, <strong><code>min_users_per_group</code></strong>=<em><code>1000</code></em>, <strong><code>smd_cols</code></strong>=<em><code>['pihat']</code></em>, <strong><code>dev_cols_transformations</code></strong>=<em><code>{'pihat': &lt;function mean at 0x7f25483da0d0&gt;}</code></em>, <strong><code>dev_factor</code></strong>=<em><code>1.0</code></em>, <strong><code>verbose</code></strong>=<em><code>True</code></em>)</p>
 </blockquote>
 
 </div>

nbs/index.ipynb

@@ -26,7 +26,7 @@
     "- Sensitivity analysis to [assess robustness of causal estimates](https://amaiya.github.io/causalnlp/causalinference.html#CausalInferenceModel.evaluate_robustness)\n",
     "- Quick and simple [key driver analysis](https://amaiya.github.io/causalnlp/key_driver_analysis.html) to yield clues on potential drivers of an outcome based on predictive power, correlations, etc.\n",
     "- Can easily be applied to [\"traditional\" tabular datasets without text](https://amaiya.github.io/causalnlp/examples.html#What-is-the-causal-impact-of-having-a-PhD-on-making-over-$50K?) (i.e., datasets with only numerical and categorical variables)\n",
-    "- Includes an experimental PyTorch implementation of [CausalBert](https://arxiv.org/abs/1905.12741) by Veitch, Sridar, and Blei (based on [reference implementation](https://github.com/rpryzant/causal-bert-pytorch) by R. Pryzant.)"
+    "- Includes an experimental PyTorch implementation of [CausalBert](https://arxiv.org/abs/1905.12741) by Veitch, Sridar, and Blei (based on [reference implementation](https://github.com/rpryzant/causal-bert-pytorch) by R. Pryzant)"
    ]
   },
   {