Capitalization fix (matheusfacure#285)

Job titles do not require capitalization.

causal-inference-for-the-brave-and-true/01-Introduction-To-Causality.ipynb

@@ -38,7 +38,7 @@
     "\n",
     "However, ML is not a panacea. It can perform wonders under rigid boundaries and still fail miserably if its data deviates a little from what the model is accustomed to. To give another example from Prediction Machines, \"in many industries, low prices are associated with low sales. For example, in the hotel industry, prices are low outside the tourist season, and prices are high when demand is highest and hotels are full. Given that data, a naive prediction might suggest that increasing the price would lead to more rooms sold.\" \n",
     "\n",
-    "ML is notoriously bad at this inverse causality type of problem. They require us to answer \"what if\" questions, which Economists call counterfactuals. What would happen if I used another price instead of this price I'm currently asking for my merchandise? What would happen if I do a low sugar one instead of this low-fat diet I'm in? If you work in a bank, giving credit, you will have to figure out how changing the customer line changes your revenue. Or, if you work in the local government, you might be asked to figure out how to make the schooling system better. Should you give tablets to every kid because the era of digital knowledge tells you to? Or should you build an old-fashioned library? \n",
+    "ML is notoriously bad at this inverse causality type of problem. They require us to answer \"what if\" questions, which economists call counterfactuals. What would happen if I used another price instead of this price I'm currently asking for my merchandise? What would happen if I do a low sugar one instead of this low-fat diet I'm in? If you work in a bank, giving credit, you will have to figure out how changing the customer line changes your revenue. Or, if you work in the local government, you might be asked to figure out how to make the schooling system better. Should you give tablets to every kid because the era of digital knowledge tells you to? Or should you build an old-fashioned library? \n",
     "\n",
     "At the heart of these questions, there is a causal inquiry we wish to know the answer to. Causal questions permeate everyday problems, like figuring out how to make sales go up. Still, they also play an essential role in dilemmas that are very personal and dear to us: do I have to go to an expensive school to be successful in life (does education cause earnings)? Does immigration lower my chances of getting a job (does immigration causes unemployment to go up)? Does money transfer to the poor lower the crime rate? It doesn't matter the field you are in. It is very likely you had or will have to answer some type of causal question. Unfortunately for ML, we can't rely on correlation-type predictions to tackle them.\n",
     "\n",