fix: correct goodman2021difference cite key to goodmanbacon2021difference

The references.bib entry has always been goodmanbacon2021difference, but
four citations across three docs used a misspelled goodman2021difference
that had never resolved. Fixes the resulting bibtex.key_not_found
warnings flagged during the per-document bibliography revert build.

Author: anevolbap

docs/source/notebooks/panel-fixed-effects.ipynb

@@ -1246,7 +1246,7 @@
    "cell_type": "markdown",
    "id": "fa9a9f8b",
    "metadata": {},
-   "source": "## Connection to Difference-in-Differences\n\nTwo-way fixed effects (TWFE) - with both unit and time fixed effects - is closely related to difference-in-differences (DiD).\n\nWith a simple 2x2 design (2 groups, 2 periods, binary treatment), TWFE produces **numerically identical** estimates to the classic DiD estimator. The model:\n\n$$Y_{it} = \\alpha_i + \\gamma_t + \\beta \\cdot D_{it} + \\epsilon_{it}$$\n\nwhere:\n- $\\alpha_i$ = unit fixed effects\n- $\\gamma_t$ = time fixed effects\n- $D_{it}$ = treatment indicator\n\nis a workhorse of applied causal inference. The coefficient $\\beta$ captures the treatment effect.\n\nWith more complex designs (staggered treatment adoption, heterogeneous effects), TWFE and simple DiD can diverge. Recent econometrics research {cite:p}`goodman2021difference,dechaisemartin2020twfe` has highlighted that TWFE can give misleading results when:\n\n1. **Treatment effects vary over time** (dynamic effects)\n2. **Treatment rolls out at different times** (staggered adoption)\n3. **Treatment effects differ across units** (heterogeneity)\n\nFor these cases, consider using CausalPy's `DifferenceInDifferences` class or newer estimators designed for staggered designs.\n\n:::{seealso}\nSee the [Difference-in-Differences notebook](difference-in-differences-pymc.ipynb) for more on DiD methods in CausalPy.\n:::\n"
+   "source": "## Connection to Difference-in-Differences\n\nTwo-way fixed effects (TWFE) - with both unit and time fixed effects - is closely related to difference-in-differences (DiD).\n\nWith a simple 2x2 design (2 groups, 2 periods, binary treatment), TWFE produces **numerically identical** estimates to the classic DiD estimator. The model:\n\n$$Y_{it} = \\alpha_i + \\gamma_t + \\beta \\cdot D_{it} + \\epsilon_{it}$$\n\nwhere:\n- $\\alpha_i$ = unit fixed effects\n- $\\gamma_t$ = time fixed effects\n- $D_{it}$ = treatment indicator\n\nis a workhorse of applied causal inference. The coefficient $\\beta$ captures the treatment effect.\n\nWith more complex designs (staggered treatment adoption, heterogeneous effects), TWFE and simple DiD can diverge. Recent econometrics research {cite:p}`goodmanbacon2021difference,dechaisemartin2020twfe` has highlighted that TWFE can give misleading results when:\n\n1. **Treatment effects vary over time** (dynamic effects)\n2. **Treatment rolls out at different times** (staggered adoption)\n3. **Treatment effects differ across units** (heterogeneity)\n\nFor these cases, consider using CausalPy's `DifferenceInDifferences` class or newer estimators designed for staggered designs.\n\n:::{seealso}\nSee the [Difference-in-Differences notebook](difference-in-differences-pymc.ipynb) for more on DiD methods in CausalPy.\n:::\n"
   },
   {
    "cell_type": "markdown",

docs/source/notebooks/sensitivity_checks.md

@@ -117,7 +117,7 @@ This is the broadest check in the current API, but it is only available for PyMC
 
 ### {doc}`PreTreatmentPlaceboCheck <../api/generated/causalpy.checks.pre_treatment_placebo.PreTreatmentPlaceboCheck>`
 
-`PreTreatmentPlaceboCheck` examines pre-treatment event-study effects in staggered DiD. If negative event times are far from zero, the parallel trends story is harder to defend and the treatment effect may be biased {cite:p}`goodman2021difference,borusyak2024revisiting`.
+`PreTreatmentPlaceboCheck` examines pre-treatment event-study effects in staggered DiD. If negative event times are far from zero, the parallel trends story is harder to defend and the treatment effect may be biased {cite:p}`goodmanbacon2021difference,borusyak2024revisiting`.
 
 ## Working with check results
 

docs/source/notebooks/staggered-difference-in-differences-pymc.ipynb

@@ -18,7 +18,7 @@
     "\n",
     "$$Y_{it} = \\alpha_i + \\lambda_t + \\tau D_{it} + \\varepsilon_{it}$$\n",
     "\n",
-    "can produce biased estimates in staggered settings {cite:p}`goodman2021difference` because:\n",
+    "can produce biased estimates in staggered settings {cite:p}`goodmanbacon2021difference` because:\n",
     "\n",
     "1. Already-treated units serve as implicit controls for later-treated units\n",
     "2. The estimate is a weighted average of treatment effects that can include negative weights\n",
@@ -2122,7 +2122,7 @@
     "\n",
     "**How it works.** The check extracts all event-time estimates with negative event-time values (pre-treatment periods) from `att_event_time_`. It computes the mean and maximum absolute pre-treatment ATT and applies a threshold-based test.\n",
     "\n",
-    "**Interpreting the output.** If the mean pre-treatment ATT is close to zero, the check **passes**, providing evidence consistent with parallel trends. If it deviates, the check **fails**, suggesting that the counterfactual model may be mis-specified or that the groups were already diverging before treatment. Note that this is a necessary but not sufficient condition: passing the pre-treatment check does not guarantee that parallel trends would have held in the post-treatment period {cite:p}`goodman2021difference`."
+    "**Interpreting the output.** If the mean pre-treatment ATT is close to zero, the check **passes**, providing evidence consistent with parallel trends. If it deviates, the check **fails**, suggesting that the counterfactual model may be mis-specified or that the groups were already diverging before treatment. Note that this is a necessary but not sufficient condition: passing the pre-treatment check does not guarantee that parallel trends would have held in the post-treatment period {cite:p}`goodmanbacon2021difference`."
    ]
   },
   {