|
6 | 6 | "source": [
|
7 | 7 | "# Banking dataset with a `pymc` model\n",
|
8 | 8 | "\n",
|
| 9 | + "<div class=\"alert alert-warning\">\n", |
| 10 | + "\n", |
| 11 | + "Warning\n", |
| 12 | + "\n", |
| 13 | + "We are still refining the difference in differences analysis code. Expect improvements soon.\n", |
| 14 | + "\n", |
| 15 | + "</div>\n", |
| 16 | + "\n", |
9 | 17 | "This notebook analyses historic data on banking closures from [Richardson & Troost (2009)](http://masteringmetrics.com/wp-content/uploads/2015/02/Richardson_Troost_2009_JPE.pdf) and used as a case study for a difference in differences analysis in the [Mastering Metrics](http://www.masteringmetrics.com) book. Here, we replicate this analysis, but using Bayesian inference."
|
10 | 18 | ]
|
11 | 19 | },
|
|
650 | 658 | ]
|
651 | 659 | },
|
652 | 660 | {
|
653 |
| - "cell_type": "code", |
654 |
| - "execution_count": null, |
| 661 | + "cell_type": "markdown", |
655 | 662 | "metadata": {},
|
656 |
| - "outputs": [], |
657 | 663 | "source": [
|
658 |
| - "from causalpy.pymc_experiments import DifferenceInDifferences\n", |
659 |
| - "from causalpy.pymc_models import LinearRegression\n", |
| 664 | + "<div class=\"alert alert-info\">\n", |
660 | 665 | "\n",
|
661 |
| - "result = DifferenceInDifferences(\n", |
662 |
| - " df_long,\n", |
663 |
| - " formula=\"bib ~ 1 + district + year + district:treated\",\n", |
664 |
| - " time_variable_name=\"year\",\n", |
665 |
| - " prediction_model=LinearRegression(),\n", |
666 |
| - ")" |
667 |
| - ] |
668 |
| - }, |
669 |
| - { |
670 |
| - "cell_type": "code", |
671 |
| - "execution_count": null, |
672 |
| - "metadata": {}, |
673 |
| - "outputs": [], |
674 |
| - "source": [ |
675 |
| - "fig, ax = result.plot()" |
676 |
| - ] |
677 |
| - }, |
678 |
| - { |
679 |
| - "cell_type": "code", |
680 |
| - "execution_count": null, |
681 |
| - "metadata": {}, |
682 |
| - "outputs": [], |
683 |
| - "source": [ |
684 |
| - "result.summary()" |
| 666 | + "Note\n", |
| 667 | + "\n", |
| 668 | + "Coming soon!\n", |
| 669 | + "\n", |
| 670 | + "</div>" |
685 | 671 | ]
|
686 | 672 | },
|
687 | 673 | {
|
688 |
| - "cell_type": "code", |
689 |
| - "execution_count": null, |
| 674 | + "cell_type": "markdown", |
690 | 675 | "metadata": {},
|
691 |
| - "outputs": [], |
692 |
| - "source": [ |
693 |
| - "ax = az.plot_posterior(result.causal_impact, ref_val=0)\n", |
694 |
| - "ax.set(title=\"Posterior estimate of causal impact\");" |
695 |
| - ] |
| 676 | + "source": [] |
696 | 677 | }
|
697 | 678 | ],
|
698 | 679 | "metadata": {
|
|
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