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1 | 1 | # Example Gallery |
2 | | -## Regression Kink Design |
| 2 | +## ANCOVA |
3 | 3 | ::::{grid} 1 2 3 3 |
4 | 4 | :gutter: 3 |
5 | 5 |
|
6 | | -:::{grid-item-card} Regression kink design with `pymc` models |
| 6 | +:::{grid-item-card} ANCOVA for pre/post treatment nonequivalent group designs |
7 | 7 | :class-card: sd-card-h-100 |
8 | | -:img-top: ../_static/thumbnails/rkink_pymc.png |
9 | | -:link: rkink_pymc |
| 8 | +:img-top: ../_static/thumbnails/ancova_pymc.png |
| 9 | +:link: ancova_pymc |
10 | 10 | :link-type: doc |
11 | 11 | ::: |
12 | 12 | :::: |
|
35 | 35 | ::: |
36 | 36 | :::: |
37 | 37 |
|
38 | | -## Inverse Propensity Score Weighting |
| 38 | +## Interrupted Time Series |
39 | 39 | ::::{grid} 1 2 3 3 |
40 | 40 | :gutter: 3 |
41 | 41 |
|
42 | | -:::{grid-item-card} The Paradox of Propensity Scores in Bayesian Inference |
| 42 | +:::{grid-item-card} Excess deaths due to COVID-19 |
43 | 43 | :class-card: sd-card-h-100 |
44 | | -:img-top: ../_static/thumbnails/inv_prop_latent.png |
45 | | -:link: inv_prop_latent |
| 44 | +:img-top: ../_static/thumbnails/its_covid.png |
| 45 | +:link: its_covid |
46 | 46 | :link-type: doc |
47 | 47 | ::: |
48 | | -:::{grid-item-card} Inverse Propensity Score Weighting with `pymc` |
| 48 | +:::{grid-item-card} Bayesian Interrupted Time Series |
49 | 49 | :class-card: sd-card-h-100 |
50 | | -:img-top: ../_static/thumbnails/inv_prop_pymc.png |
51 | | -:link: inv_prop_pymc |
| 50 | +:img-top: ../_static/thumbnails/its_pymc.png |
| 51 | +:link: its_pymc |
| 52 | +:link-type: doc |
| 53 | +::: |
| 54 | +:::{grid-item-card} Interrupted Time Series (ITS) with scikit-learn models |
| 55 | +:class-card: sd-card-h-100 |
| 56 | +:img-top: ../_static/thumbnails/its_skl.png |
| 57 | +:link: its_skl |
52 | 58 | :link-type: doc |
53 | 59 | ::: |
54 | 60 | :::: |
55 | 61 |
|
56 | | -## ANCOVA |
| 62 | +## Geographical lift testing |
57 | 63 | ::::{grid} 1 2 3 3 |
58 | 64 | :gutter: 3 |
59 | 65 |
|
60 | | -:::{grid-item-card} ANCOVA for pre/post treatment nonequivalent group designs |
| 66 | +:::{grid-item-card} Bayesian geolift with CausalPy |
61 | 67 | :class-card: sd-card-h-100 |
62 | | -:img-top: ../_static/thumbnails/ancova_pymc.png |
63 | | -:link: ancova_pymc |
| 68 | +:img-top: ../_static/thumbnails/geolift1.png |
| 69 | +:link: geolift1 |
| 70 | +:link-type: doc |
| 71 | +::: |
| 72 | +:::{grid-item-card} Multi-cell geolift analysis |
| 73 | +:class-card: sd-card-h-100 |
| 74 | +:img-top: ../_static/thumbnails/multi_cell_geolift.png |
| 75 | +:link: multi_cell_geolift |
| 76 | +:link-type: doc |
| 77 | +::: |
| 78 | +:::: |
| 79 | + |
| 80 | +## Regression Discontinuity |
| 81 | +::::{grid} 1 2 3 3 |
| 82 | +:gutter: 3 |
| 83 | + |
| 84 | +:::{grid-item-card} Sharp regression discontinuity with `pymc` models |
| 85 | +:class-card: sd-card-h-100 |
| 86 | +:img-top: ../_static/thumbnails/rd_pymc.png |
| 87 | +:link: rd_pymc |
| 88 | +:link-type: doc |
| 89 | +::: |
| 90 | +:::{grid-item-card} Drinking age - Bayesian analysis |
| 91 | +:class-card: sd-card-h-100 |
| 92 | +:img-top: ../_static/thumbnails/rd_pymc_drinking.png |
| 93 | +:link: rd_pymc_drinking |
| 94 | +:link-type: doc |
| 95 | +::: |
| 96 | +:::{grid-item-card} Sharp regression discontinuity with scikit-learn models |
| 97 | +:class-card: sd-card-h-100 |
| 98 | +:img-top: ../_static/thumbnails/rd_skl.png |
| 99 | +:link: rd_skl |
| 100 | +:link-type: doc |
| 101 | +::: |
| 102 | +:::{grid-item-card} Drinking age with a scikit-learn model |
| 103 | +:class-card: sd-card-h-100 |
| 104 | +:img-top: ../_static/thumbnails/rd_skl_drinking.png |
| 105 | +:link: rd_skl_drinking |
64 | 106 | :link-type: doc |
65 | 107 | ::: |
66 | 108 | :::: |
|
83 | 125 | ::: |
84 | 126 | :::: |
85 | 127 |
|
86 | | -## Interrupted Time Series |
| 128 | +## Regression Kink Design |
87 | 129 | ::::{grid} 1 2 3 3 |
88 | 130 | :gutter: 3 |
89 | 131 |
|
90 | | -:::{grid-item-card} Excess deaths due to COVID-19 |
91 | | -:class-card: sd-card-h-100 |
92 | | -:img-top: ../_static/thumbnails/its_covid.png |
93 | | -:link: its_covid |
94 | | -:link-type: doc |
95 | | -::: |
96 | | -:::{grid-item-card} Bayesian Interrupted Time Series |
97 | | -:class-card: sd-card-h-100 |
98 | | -:img-top: ../_static/thumbnails/its_pymc.png |
99 | | -:link: its_pymc |
100 | | -:link-type: doc |
101 | | -::: |
102 | | -:::{grid-item-card} Interrupted Time Series (ITS) with scikit-learn models |
| 132 | +:::{grid-item-card} Regression kink design with `pymc` models |
103 | 133 | :class-card: sd-card-h-100 |
104 | | -:img-top: ../_static/thumbnails/its_skl.png |
105 | | -:link: its_skl |
| 134 | +:img-top: ../_static/thumbnails/rkink_pymc.png |
| 135 | +:link: rkink_pymc |
106 | 136 | :link-type: doc |
107 | 137 | ::: |
108 | 138 | :::: |
|
131 | 161 | ::: |
132 | 162 | :::: |
133 | 163 |
|
134 | | -## Regression Discontinuity |
135 | | -::::{grid} 1 2 3 3 |
136 | | -:gutter: 3 |
137 | | - |
138 | | -:::{grid-item-card} Sharp regression discontinuity with `pymc` models |
139 | | -:class-card: sd-card-h-100 |
140 | | -:img-top: ../_static/thumbnails/rd_pymc.png |
141 | | -:link: rd_pymc |
142 | | -:link-type: doc |
143 | | -::: |
144 | | -:::{grid-item-card} Drinking age - Bayesian analysis |
145 | | -:class-card: sd-card-h-100 |
146 | | -:img-top: ../_static/thumbnails/rd_pymc_drinking.png |
147 | | -:link: rd_pymc_drinking |
148 | | -:link-type: doc |
149 | | -::: |
150 | | -:::{grid-item-card} Sharp regression discontinuity with scikit-learn models |
151 | | -:class-card: sd-card-h-100 |
152 | | -:img-top: ../_static/thumbnails/rd_skl.png |
153 | | -:link: rd_skl |
154 | | -:link-type: doc |
155 | | -::: |
156 | | -:::{grid-item-card} Drinking age with a scikit-learn model |
157 | | -:class-card: sd-card-h-100 |
158 | | -:img-top: ../_static/thumbnails/rd_skl_drinking.png |
159 | | -:link: rd_skl_drinking |
160 | | -:link-type: doc |
161 | | -::: |
162 | | -:::: |
163 | | - |
164 | | -## Geographical lift testing |
| 164 | +## Inverse Propensity Score Weighting |
165 | 165 | ::::{grid} 1 2 3 3 |
166 | 166 | :gutter: 3 |
167 | 167 |
|
168 | | -:::{grid-item-card} Bayesian geolift with CausalPy |
| 168 | +:::{grid-item-card} The Paradox of Propensity Scores in Bayesian Inference |
169 | 169 | :class-card: sd-card-h-100 |
170 | | -:img-top: ../_static/thumbnails/geolift1.png |
171 | | -:link: geolift1 |
| 170 | +:img-top: ../_static/thumbnails/inv_prop_latent.png |
| 171 | +:link: inv_prop_latent |
172 | 172 | :link-type: doc |
173 | 173 | ::: |
174 | | -:::{grid-item-card} Multi-cell geolift analysis |
| 174 | +:::{grid-item-card} Inverse Propensity Score Weighting with `pymc` |
175 | 175 | :class-card: sd-card-h-100 |
176 | | -:img-top: ../_static/thumbnails/multi_cell_geolift.png |
177 | | -:link: multi_cell_geolift |
| 176 | +:img-top: ../_static/thumbnails/inv_prop_pymc.png |
| 177 | +:link: inv_prop_pymc |
178 | 178 | :link-type: doc |
179 | 179 | ::: |
180 | 180 | :::: |
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