fixing typos

Signed-off-by: Nathaniel <[email protected]>

Author: NathanielF

docs/source/notebooks/inv_prop_latent.ipynb

@@ -588,7 +588,7 @@
     "\n",
     "Here we allow for a function that takes the same inputs but fits two separate models. First we fit the treatment model then store the `idata_treatment` this xarray object stores the posterior estimates for the propensity score. We pass this through to a second outcome model where we proceed to take a random draw from the posterior and pass it through to the outcome regression via a spline component. This allows us to express any non-linearity in the treatment effect. Additionally it can be seen as a way to augment the outcome model.\n",
     "\n",
-    "While theoretically the propensity score contains no extra information if we are already conditioning on $X$, practically the literature reports that the propensity improves the stability of the causal estimates achievable in Bayesian causal modelling. Additionally we might want to seperate covariates for predicting the outcome and the treatment. In this case, there may be extra information derived in the treatment model that be used to inform the outcome model. "
+    "While theoretically the propensity score contains no extra information if we are already conditioning on $X$, practically the literature reports that the propensity improves the stability of the causal estimates achievable in Bayesian causal modelling. Additionally we might want to separate covariates for predicting the outcome and the treatment. In this case, there may be extra information derived in the treatment model that be used to inform the outcome model. "
    ]
   },
   {
@@ -1242,7 +1242,7 @@
     "\n",
     "#### Propensity Score Quantiles - Joint Model\n",
     "\n",
-    "We can see how the different model specifications have yielded distinct propensity score estimates - as the joint specification seems to compensate for missing covariates in the outcome model by adjusting the propensity score latent in the treatment model too. We can see the diffferences in the quantiles to highlight a numeric difference in the propensity score distributions between the two models."
+    "We can see how the different model specifications have yielded distinct propensity score estimates - as the joint specification seems to compensate for missing covariates in the outcome model by adjusting the propensity score latent in the treatment model too. We can see the differences in the quantiles to highlight a numeric difference in the propensity score distributions between the two models."
    ]
   },
   {
@@ -2228,7 +2228,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "First we specify our counterfactual input data. Then we push them throug the joint model distribution using the do-operator in `PyMC` to sample from the posterior predictive distribution giving us sample of the potential outcomes $Y(1), Y(0)$"
+    "First we specify our counterfactual input data. Then we push them through the joint model distribution using the do-operator in `PyMC` to sample from the posterior predictive distribution giving us sample of the potential outcomes $Y(1), Y(0)$"
    ]
   },
   {