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Merge pull request #25 from chrisrijsdijk/Arduino3Dsimple
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Causal inference site text "41"
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chrisrijsdijk committed Jul 18, 2023
2 parents b4f47b2 + 9906fa5 commit 6978003
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8 changes: 4 additions & 4 deletions sitetext/.ipynb_checkpoints/CausalInference-checkpoint.ipynb
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" <td>[39:40] <td> 0,0 <td> 0,0 <td> 0\n",
" </tr>\n",
" <tr>\n",
" <td>[40:49] <td> 2,5 <td> 2,5 <td> 1\n",
" <td>[41:49] <td> 2,5 <td> 2,5 <td> 1\n",
" </tr>\n",
" <tr>\n",
" <td>[50:53] <td> 3,5 <td> 1,5 <td> 1\n",
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" <td>[39:40] <td> 0,0 <td> 0,0 <td> 0 <td> 1\n",
" </tr>\n",
" <tr>\n",
" <td>[40:49] <td> 2,5 <td> 2,5 <td> 1 <td> 1\n",
" <td>[41:49] <td> 2,5 <td> 2,5 <td> 1 <td> 1\n",
" </tr>\n",
" <tr>\n",
" <td>[50:53] <td> 3,5 <td> 1,5 <td> 1 <td> 0\n",
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"$ E[V_{0}|S_{1}=1,B=0]-E[V_{0}|S_{1}=0,B=0] = 3,44V \\;\\;\\;\\;\\;\\;\\;-^{DAG}\\rightarrow\\;\\;\\;\\;\\;\\;\\; ATE_{@B=0} = E[V_{0}(S_{1}=1)|B=0]-E[V_{0}(S_{1}=0)|B=0]=3,44V $\n",
"$ E[V_{0}|S_{1}=1,B=1]-E[V_{0}|S_{1}=0,B=1] = 2,50V \\;\\;\\;\\;\\;\\;\\;-^{DAG}\\rightarrow\\;\\;\\;\\;\\;\\;\\; ATE_{@B=1} = E[V_{0}(S_{1}=1)|B=1]-E[V_{0}(S_{1}=0)|B=1]=2,50V $\n",
"\n",
"Up until now, the time series $B$ remained an unobserved hidden variable. But an engineer may identify the anomalous behaviour at $t=[40:49]$ as a short circuit of the light. Eventually, the occurrence of this hidden failure mode has been recorded which enables verification. Generally, any causal inference from observations is vulnerable for unobserved background variables as observations are incomplete. The presumed DAG may be highly controversial in some cases, but at least it is a precise description of the expert knowledge required to causaly explain this statistical association.\n",
"Up until now, the time series $B$ remained an unobserved hidden variable. But an engineer may identify the anomalous behaviour at $t=[41:49]$ as a short circuit of the light. Eventually, the occurrence of this hidden failure mode has been recorded which enables verification. Generally, any causal inference from observations is vulnerable for unobserved background variables as observations are incomplete. The presumed DAG may be highly controversial in some cases, but at least it is a precise description of the expert knowledge required to causaly explain this statistical association.\n",
"\n",
"\n",
"## Example of causal inference extended with multiple failure modes\n",
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{
"cell_type": "code",
"execution_count": null,
"id": "a3903ce4-86c9-4dc9-a3f7-edf759a9eb8f",
"id": "a93d2a49-a365-4a2c-bb1d-9f15ca613ccb",
"metadata": {},
"outputs": [],
"source": []
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8 changes: 4 additions & 4 deletions sitetext/CausalInference.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -45,7 +45,7 @@
" <td>[39:40] <td> 0,0 <td> 0,0 <td> 0\n",
" </tr>\n",
" <tr>\n",
" <td>[40:49] <td> 2,5 <td> 2,5 <td> 1\n",
" <td>[41:49] <td> 2,5 <td> 2,5 <td> 1\n",
" </tr>\n",
" <tr>\n",
" <td>[50:53] <td> 3,5 <td> 1,5 <td> 1\n",
Expand Down Expand Up @@ -142,7 +142,7 @@
" <td>[39:40] <td> 0,0 <td> 0,0 <td> 0 <td> 1\n",
" </tr>\n",
" <tr>\n",
" <td>[40:49] <td> 2,5 <td> 2,5 <td> 1 <td> 1\n",
" <td>[41:49] <td> 2,5 <td> 2,5 <td> 1 <td> 1\n",
" </tr>\n",
" <tr>\n",
" <td>[50:53] <td> 3,5 <td> 1,5 <td> 1 <td> 0\n",
Expand Down Expand Up @@ -177,7 +177,7 @@
"$ E[V_{0}|S_{1}=1,B=0]-E[V_{0}|S_{1}=0,B=0] = 3,44V \\;\\;\\;\\;\\;\\;\\;-^{DAG}\\rightarrow\\;\\;\\;\\;\\;\\;\\; ATE_{@B=0} = E[V_{0}(S_{1}=1)|B=0]-E[V_{0}(S_{1}=0)|B=0]=3,44V $\n",
"$ E[V_{0}|S_{1}=1,B=1]-E[V_{0}|S_{1}=0,B=1] = 2,50V \\;\\;\\;\\;\\;\\;\\;-^{DAG}\\rightarrow\\;\\;\\;\\;\\;\\;\\; ATE_{@B=1} = E[V_{0}(S_{1}=1)|B=1]-E[V_{0}(S_{1}=0)|B=1]=2,50V $\n",
"\n",
"Up until now, the time series $B$ remained an unobserved hidden variable. But an engineer may identify the anomalous behaviour at $t=[40:49]$ as a short circuit of the light. Eventually, the occurrence of this hidden failure mode has been recorded which enables verification. Generally, any causal inference from observations is vulnerable for unobserved background variables as observations are incomplete. The presumed DAG may be highly controversial in some cases, but at least it is a precise description of the expert knowledge required to causaly explain this statistical association.\n",
"Up until now, the time series $B$ remained an unobserved hidden variable. But an engineer may identify the anomalous behaviour at $t=[41:49]$ as a short circuit of the light. Eventually, the occurrence of this hidden failure mode has been recorded which enables verification. Generally, any causal inference from observations is vulnerable for unobserved background variables as observations are incomplete. The presumed DAG may be highly controversial in some cases, but at least it is a precise description of the expert knowledge required to causaly explain this statistical association.\n",
"\n",
"\n",
"## Example of causal inference extended with multiple failure modes\n",
Expand Down Expand Up @@ -238,7 +238,7 @@
{
"cell_type": "code",
"execution_count": null,
"id": "a3903ce4-86c9-4dc9-a3f7-edf759a9eb8f",
"id": "a93d2a49-a365-4a2c-bb1d-9f15ca613ccb",
"metadata": {},
"outputs": [],
"source": []
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