Update cross-references to new interventional_what_if_do_operator label

Anticipates PR #850 renaming counterfactuals_do_operator to
interventional_what_if_do_operator. Both PRs should merge together.

Made-with: Cursor

Author: drbenvincent

examples/causal_inference/difference_in_differences.ipynb

@@ -82,7 +82,7 @@
         "\n",
         "This differs from Pearl's **Level 3** (unit-level) counterfactuals {cite:p}`pearl2009causality`, which require *abduction* — inferring unit-specific exogenous variables from observed data and then reasoning about what would have happened to *that particular unit* under a different action. The difference-in-differences approach operates at Level 2 (interventional) in Pearl's causal hierarchy, making \"counterfactual\" in the Rubin sense the appropriate term.\n",
         "\n",
-        "For a detailed discussion of the distinction between interventional (L2) and counterfactual (L3) reasoning, see the {ref}`counterfactuals_do_operator` notebook.\n",
+        "For a detailed discussion of the distinction between interventional (L2) and counterfactual (L3) reasoning, see the {ref}`interventional_what_if_do_operator` notebook.\n",
         ":::"
       ],
       "id": "9b827bb1-ce58-436c-b4d0-e4931c4f1829"

examples/causal_inference/difference_in_differences.myst.md

@@ -56,7 +56,7 @@ This notebook uses "counterfactual" in the **potential outcomes** (Rubin) sense
 
 This differs from Pearl's **Level 3** (unit-level) counterfactuals {cite:p}`pearl2009causality`, which require *abduction* — inferring unit-specific exogenous variables from observed data and then reasoning about what would have happened to *that particular unit* under a different action. The difference-in-differences approach operates at Level 2 (interventional) in Pearl's causal hierarchy, making "counterfactual" in the Rubin sense the appropriate term.
 
-For a detailed discussion of the distinction between interventional (L2) and counterfactual (L3) reasoning, see the {ref}`counterfactuals_do_operator` notebook.
+For a detailed discussion of the distinction between interventional (L2) and counterfactual (L3) reasoning, see the {ref}`interventional_what_if_do_operator` notebook.
 :::
 
 +++

examples/causal_inference/excess_deaths.ipynb

@@ -32,7 +32,7 @@
         "\n",
         "This differs from Pearl's **Level 3** (unit-level) counterfactuals {cite:p}`pearl2009causality`, which require *abduction* — inferring unit-specific exogenous variables from observed data and then reasoning about what would have happened to *that particular unit* under a different action. The forecasting approach used here operates at Level 2 (interventional) in Pearl's causal hierarchy, making \"counterfactual\" in the Rubin sense the appropriate term.\n",
         "\n",
-        "For a detailed discussion of the distinction between interventional (L2) and counterfactual (L3) reasoning, see the {ref}`counterfactuals_do_operator` notebook.\n",
+        "For a detailed discussion of the distinction between interventional (L2) and counterfactual (L3) reasoning, see the {ref}`interventional_what_if_do_operator` notebook.\n",
         ":::"
       ],
       "id": "a15cd228-d1cd-4d52-bc62-92aa975f798c"

examples/causal_inference/excess_deaths.myst.md

@@ -38,7 +38,7 @@ This notebook uses "counterfactual" in the **potential outcomes** (Rubin) sense
 
 This differs from Pearl's **Level 3** (unit-level) counterfactuals {cite:p}`pearl2009causality`, which require *abduction* — inferring unit-specific exogenous variables from observed data and then reasoning about what would have happened to *that particular unit* under a different action. The forecasting approach used here operates at Level 2 (interventional) in Pearl's causal hierarchy, making "counterfactual" in the Rubin sense the appropriate term.
 
-For a detailed discussion of the distinction between interventional (L2) and counterfactual (L3) reasoning, see the {ref}`counterfactuals_do_operator` notebook.
+For a detailed discussion of the distinction between interventional (L2) and counterfactual (L3) reasoning, see the {ref}`interventional_what_if_do_operator` notebook.
 :::
 
 +++

examples/causal_inference/interrupted_time_series.ipynb

@@ -32,7 +32,7 @@
         "\n",
         "This differs from Pearl's **Level 3** (unit-level) counterfactuals {cite:p}`pearl2009causality`, which require *abduction* — inferring unit-specific exogenous variables from observed data and then reasoning about what would have happened to *that particular unit* under a different action. The forecasting approach used here operates at Level 2 (interventional) in Pearl's causal hierarchy, making \"counterfactual\" in the Rubin sense the appropriate term.\n",
         "\n",
-        "For a detailed discussion of the distinction between interventional (L2) and counterfactual (L3) reasoning, see the {ref}`counterfactuals_do_operator` notebook.\n",
+        "For a detailed discussion of the distinction between interventional (L2) and counterfactual (L3) reasoning, see the {ref}`interventional_what_if_do_operator` notebook.\n",
         ":::\n",
         "\n",
         "Interested readers are directed to the excellent textbook [The Effect](https://theeffectbook.net/) {cite:p}`huntington2021effect`. Chapter 17 covers 'event studies' which the author prefers to the interrupted time series terminology."

examples/causal_inference/interrupted_time_series.myst.md

@@ -34,7 +34,7 @@ This notebook uses "counterfactual" in the **potential outcomes** (Rubin) sense
 
 This differs from Pearl's **Level 3** (unit-level) counterfactuals {cite:p}`pearl2009causality`, which require *abduction* — inferring unit-specific exogenous variables from observed data and then reasoning about what would have happened to *that particular unit* under a different action. The forecasting approach used here operates at Level 2 (interventional) in Pearl's causal hierarchy, making "counterfactual" in the Rubin sense the appropriate term.
 
-For a detailed discussion of the distinction between interventional (L2) and counterfactual (L3) reasoning, see the {ref}`counterfactuals_do_operator` notebook.
+For a detailed discussion of the distinction between interventional (L2) and counterfactual (L3) reasoning, see the {ref}`interventional_what_if_do_operator` notebook.
 :::
 
 Interested readers are directed to the excellent textbook [The Effect](https://theeffectbook.net/) {cite:p}`huntington2021effect`. Chapter 17 covers 'event studies' which the author prefers to the interrupted time series terminology.

examples/causal_inference/regression_discontinuity.ipynb

@@ -615,7 +615,7 @@
         "\n",
         "This differs from Pearl's **Level 3** (unit-level) counterfactuals {cite:p}`pearl2009causality`, which require *abduction* — inferring unit-specific exogenous variables from observed data and then reasoning about what would have happened to *that particular unit* under a different action. The regression discontinuity approach operates at Level 2 (interventional) in Pearl's causal hierarchy, making \"counterfactual\" in the Rubin sense the appropriate term.\n",
         "\n",
-        "For a detailed discussion of the distinction between interventional (L2) and counterfactual (L3) reasoning, see the {ref}`counterfactuals_do_operator` notebook.\n",
+        "For a detailed discussion of the distinction between interventional (L2) and counterfactual (L3) reasoning, see the {ref}`interventional_what_if_do_operator` notebook.\n",
         ":::\n",
         "\n",
         "_Technical note:_ Formally we are doing posterior prediction of `y`. Running `pm.sample_posterior_predictive` multiple times with different random seeds will result in new and different samples of `y` each time. However, this is not the case (we are not formally doing posterior prediction) for `mu`. This is because `mu` is a deterministic function (`mu = x + delta*treated`), so for our already obtained posterior samples of `delta`, the values of `mu` will be entirely determined by the values of `x` and `treated` data)."

examples/causal_inference/regression_discontinuity.myst.md

@@ -171,7 +171,7 @@ This notebook uses "counterfactual" in the **potential outcomes** (Rubin) sense
 
 This differs from Pearl's **Level 3** (unit-level) counterfactuals {cite:p}`pearl2009causality`, which require *abduction* — inferring unit-specific exogenous variables from observed data and then reasoning about what would have happened to *that particular unit* under a different action. The regression discontinuity approach operates at Level 2 (interventional) in Pearl's causal hierarchy, making "counterfactual" in the Rubin sense the appropriate term.
 
-For a detailed discussion of the distinction between interventional (L2) and counterfactual (L3) reasoning, see the {ref}`counterfactuals_do_operator` notebook.
+For a detailed discussion of the distinction between interventional (L2) and counterfactual (L3) reasoning, see the {ref}`interventional_what_if_do_operator` notebook.
 :::
 
 _Technical note:_ Formally we are doing posterior prediction of `y`. Running `pm.sample_posterior_predictive` multiple times with different random seeds will result in new and different samples of `y` each time. However, this is not the case (we are not formally doing posterior prediction) for `mu`. This is because `mu` is a deterministic function (`mu = x + delta*treated`), so for our already obtained posterior samples of `delta`, the values of `mu` will be entirely determined by the values of `x` and `treated` data).