tidying spell check

Signed-off-by: Nathaniel <NathanielF@users.noreply.github.com>

Author: NathanielF

docs/source/knowledgebase/structural_causal_models.ipynb

@@ -1649,7 +1649,7 @@
  "cell_type": "markdown",
  "metadata": {},
  "source": [
- "In the plot we can see that the majority of models accurately estimate the true treatment effect $\\alpha$ except in the cases where we have explictly placed an opinionated prior on the $\\rho$ parameter in the model. This priors pulls the $\\alpha$ estimate away from the true data generating process. "
+ "In the plot we can see that the majority of models accurately estimate the true treatment effect $\\alpha$ except in the cases where we have explicitly placed an opinionated prior on the $\\rho$ parameter in the model. This priors pulls the $\\alpha$ estimate away from the true data generating process. "
  ]
  },
  {
@@ -2486,7 +2486,7 @@
  "\n",
  "### Causal Identification and Variable Selection\n",
  "\n",
- "Before continuing to the binary case it's worth diving into the role of priors in these structural causal models. Both spike and slab and horseshoe priors were designed to perform automatic variable selection. Tthe spike-and-slab via a latent mixture of near-zero and freely estimated components, and the horseshoe through continuous shrinkage that allows strong predictors to survive while damping weak or spurious ones. Plotting these posteriors vividly illustrates their behavior more clearly than describing it. "
+ "Before continuing to the binary case it's worth diving into the role of priors in these structural causal models. Both spike and slab and horseshoe priors were designed to perform automatic variable selection. The spike-and-slab via a latent mixture of near-zero and freely estimated components, and the horseshoe through continuous shrinkage that allows strong predictors to survive while damping weak or spurious ones. Plotting these posteriors vividly illustrates their behavior more clearly than describing it. "
  ]
  },
  {