Added missing period (matheusfacure#284)

causal-inference-for-the-brave-and-true/02-Randomised-Experiments.ipynb

@@ -16,7 +16,7 @@
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-    "To recap, association becomes causation if there is no bias. There will be no bias if $E[Y_0|T=0]=E[Y_0|T=1]$. In words, association will be causation if the treated and control are equal or comparable, except for their treatment. Or, in more technical words, when the outcome of the untreated is equal to the counterfactual outcome of the treated. Remember that this counterfactual outcome is the outcome of the treated group if they had not received the treatment\n",
+    "To recap, association becomes causation if there is no bias. There will be no bias if $E[Y_0|T=0]=E[Y_0|T=1]$. In words, association will be causation if the treated and control are equal or comparable, except for their treatment. Or, in more technical words, when the outcome of the untreated is equal to the counterfactual outcome of the treated. Remember that this counterfactual outcome is the outcome of the treated group if they had not received the treatment.\n",
     "\n",
     "I think we did an OK job explaining how to make association equal to causation in math terms. But that was only in theory. Now, we look at the first tool we have to make the bias vanish: **Randomised Experiments**. Randomised experiments randomly assign individuals in a population to a treatment or to a control group. The proportion that receives the treatment doesn't have to be 50%. You could have an experiment where only 10% of your samples get the treatment.\n",
     "\n",