fix typo

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

Author: NathanielF

docs/source/knowledgebase/structural_causal_models.ipynb

@@ -1664,7 +1664,7 @@
    "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 explicitly placed an opinionated prior on the $\\rho$ parameter in the model. These priors pull the $\\alpha$ estimate away from the true data generating process. The variable selection priors considerably shrink the uncertainty in the treatment estimates seemingly picking out the implict instrument structure aping the application of instrumental variables. \n",
+    "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. These priors pull the $\\alpha$ estimate away from the true data generating process. The variable selection priors considerably shrink the uncertainty in the treatment estimates seemingly picking out the implicit instrument structure aping the application of instrumental variables. \n",
     "\n",
     "Our Bayesian setup here is intentionally structural. We specify how both treatment and outcome arise from common covariates and latent confounding structures. However, the boundary between structural and reduced-form reasoning becomes fluid when we begin to treat latent variables or exclusion restrictions as data-driven “instruments.” In that sense, the structural Bayesian approach can emulate reduced-form logic within a generative model — an idea we’ll develop further when we move from unconfounded to confounded data and later when we impute potential outcomes directly. \n",
     "\n",