Merge pull request #23 from AndreaChlebikova/master

Fixing grammar issues and typos in example notebooks

docs/source/do_why_confounder_example.ipynb

@@ -6,7 +6,7 @@
    "source": [
     "# Confounding Example: Finding causal effects from observed data\n",
     "\n",
-    "Suppose you are given some data with treatment and outcome. Can you determine whether the treatment causes the outcome, or is the correlation purely due to another common cause?"
+    "Suppose you are given some data with treatment and outcome. Can you determine whether the treatment causes the outcome, or the correlation is purely due to another common cause?"
    ]
   },
   {
@@ -39,9 +39,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Let's create a mystery dataset. Need to find if there is a causal effect.\n",
+    "# Let's create a mystery dataset for which we need to determine whether there is a causal effect.\n",
     "\n",
-    "Creating the dataset. It is generated from either of two models:\n",
+    "Creating the dataset. It is generated from either one of two models:\n",
     "* **Model 1**: Treatment does cause outcome. \n",
     "* **Model 2**: Treatment does not cause outcome. All observed correlation is due to a common cause."
    ]
@@ -99,7 +99,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Using DoWhy to resolve the mystery: *Does Treatment cause Outcome variable?*\n",
+    "# Using DoWhy to resolve the mystery: *Does Treatment cause Outcome?*\n",
     "## STEP 1: Model the problem as a causal graph\n",
     "Initializing the causal model."
    ]
@@ -138,7 +138,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Showing the causal model stored in local file \"causal_model.png\""
+    "Showing the causal model stored in the local file \"causal_model.png\""
    ]
   },
   {
@@ -229,7 +229,7 @@
    "source": [
     "## STEP 3: Estimate the causal effect\n",
     "\n",
-    "Once we have the identified estimand, can use any statistical method to estimate the causal effect. \n",
+    "Once we have identified the estimand, we can use any statistical method to estimate the causal effect. \n",
     "\n",
     "Let's use Linear Regression for simplicity."
    ]

docs/source/do_why_estimation_methods.ipynb

@@ -5,10 +5,10 @@
    "metadata": {},
    "source": [
     "# DoWhy: Different estimation methods for causal inference\n",
-    "This is quick introduction to DoWhy causal inference library.\n",
+    "This is a quick introduction to the DoWhy causal inference library.\n",
-    "We will load in a sample dataset and use different methods for estimating causal effect from a (pre-specified)treatment variable to a (pre-specified) outcome variable.\n",
+    "We will load in a sample dataset and use different methods for estimating the causal effect of a (pre-specified)treatment variable on a (pre-specified) outcome variable.\n",
     "\n",
-    "First, let us add required path for python to find DoWhy code and load required packages."
+    "First, let us add the required path for Python to find the DoWhy code and load all required packages"
    ]
   },
   {
@@ -40,7 +40,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Let us first load a dataset. For simplicity, we simulate a dataset with linear relationships between common causes and treatment, and common causes and outcome. \n",
+    "Now, let us load a dataset. For simplicity, we simulate a dataset with linear relationships between common causes and treatment, and common causes and outcome. \n",
     "\n",
     "Beta is the true causal effect. "
    ]
@@ -464,7 +464,7 @@
    "source": [
     "## Method 5: Instrumental Variable\n",
     "\n",
-    "We will be using Wald estimator for the provided instrumental variable."
+    "We will be using the Wald estimator for the provided instrumental variable."
    ]
   },
   {

docs/source/do_why_simple_example.ipynb

@@ -5,10 +5,10 @@
    "metadata": {},
    "source": [
     "# Getting started with DoWhy: A simple example\n",
-    "This is quick introduction to DoWhy causal inference library.\n",
+    "This is a quick introduction to the DoWhy causal inference library.\n",
-    "We will load in a sample dataset and estimate causal effect from a (pre-specified)treatment variable to a (pre-specified) outcome variable.\n",
+    "We will load in a sample dataset and estimate the causal effect of a (pre-specified)treatment variable on a (pre-specified) outcome variable.\n",
     "\n",
-    "First, let us add required path for python to find DoWhy code and load required packages."
+    "First, let us add the required path for Python to find the DoWhy code and load all required packages."
    ]
   },
   {
@@ -39,7 +39,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Let us first load a dataset. For simplicity, we simulate a dataset with linear relationships between common causes and treatment, and common causes and outcome. \n",
+    "Now, let us load a dataset. For simplicity, we simulate a dataset with linear relationships between common causes and treatment, and common causes and outcome. \n",
     "\n",
     "Beta is the true causal effect. "
    ]
@@ -166,7 +166,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The above causal graph shows the assumptions encoded in the cauasl model. We can now use this graph to first identify \n",
+    "The above causal graph shows the assumptions encoded in the causal model. We can now use this graph to first identify \n",
     "the causal effect (go from a causal estimand to a probability expression), and then estimate the causal effect."
    ]
   },
@@ -176,8 +176,8 @@
    "source": [
     "**DoWhy philosophy: Keep identification and estimation separate**\n",
     "\n",
-    "Identification can be achieved without access to data, only the graph. This results in an expression to computed. This expression can then be computed using the available data in the estimation step.\n",
+    "Identification can be achieved without access to the data, acccesing only the graph. This results in an expression to be computed. This expression can then be evaluated using the available data in the estimation step.\n",
-    "Important to understand that these are orthogonal steps.\n",
+    "It is important to understand that these are orthogonal steps.\n",
     "\n",
     "* Identification"
    ]
@@ -474,7 +474,7 @@
    "source": [
     "## Refuting the estimate\n",
     "\n",
-    "Now refuting the obtained estimate."
+    "Let us now look at ways of refuting the estimate obtained."
    ]
   },
   {