What Is Causal Inference?:
Causal inference lies at the heart of our ability to understand why things happen by helping us predict the result of any action. This process is vital for businesses that aspire to turn data and information into valuable knowledge. With this report, data scientists and analysts will learn a princip...
Gespeichert in:
Beteiligte Personen: | , |
---|---|
Körperschaften: | , |
Format: | Elektronisch E-Book |
Sprache: | Englisch |
Veröffentlicht: |
[Erscheinungsort nicht ermittelbar]
O'Reilly Media, Inc.
2022
|
Ausgabe: | 1st edition. |
Schlagwörter: | |
Links: | https://learning.oreilly.com/library/view/-/9781098118990/?ar |
Zusammenfassung: | Causal inference lies at the heart of our ability to understand why things happen by helping us predict the result of any action. This process is vital for businesses that aspire to turn data and information into valuable knowledge. With this report, data scientists and analysts will learn a principled way of thinking about causality using a suite of causal inference techniques now available. Authors Hugo Bowne-Anderson, a data science consultant, and Mike Loukides, vice president of content strategy at O'Reilly Media, introduce causality and discuss randomized control trials (RCTs), key aspects of causal graph theory, and well-needed techniques from econometrics. |
Beschreibung: | Online resource; Title from title page (viewed January 25, 2022) |
Umfang: | 1 Online-Ressource (40 Seiten) |
ISBN: | 9781098118990 1098118995 |
Internformat
MARC
LEADER | 00000cam a22000002 4500 | ||
---|---|---|---|
001 | ZDB-30-ORH-076599329 | ||
003 | DE-627-1 | ||
005 | 20240228121545.0 | ||
007 | cr uuu---uuuuu | ||
008 | 220209s2022 xx |||||o 00| ||eng c | ||
020 | |a 9781098118990 |9 978-1-0981-1899-0 | ||
020 | |a 1098118995 |9 1-0981-1899-5 | ||
035 | |a (DE-627-1)076599329 | ||
035 | |a (DE-599)KEP076599329 | ||
035 | |a (ORHE)9781098118990 | ||
035 | |a (DE-627-1)076599329 | ||
040 | |a DE-627 |b ger |c DE-627 |e rda | ||
041 | |a eng | ||
082 | 0 | |a 519.5/4 |2 23 | |
100 | 1 | |a Bowne-Anderson, Hugo |e VerfasserIn |4 aut | |
245 | 1 | 0 | |a What Is Causal Inference? |c Bowne-Anderson, Hugo |
250 | |a 1st edition. | ||
264 | 1 | |a [Erscheinungsort nicht ermittelbar] |b O'Reilly Media, Inc. |c 2022 | |
300 | |a 1 Online-Ressource (40 Seiten) | ||
336 | |a Text |b txt |2 rdacontent | ||
337 | |a Computermedien |b c |2 rdamedia | ||
338 | |a Online-Ressource |b cr |2 rdacarrier | ||
500 | |a Online resource; Title from title page (viewed January 25, 2022) | ||
520 | |a Causal inference lies at the heart of our ability to understand why things happen by helping us predict the result of any action. This process is vital for businesses that aspire to turn data and information into valuable knowledge. With this report, data scientists and analysts will learn a principled way of thinking about causality using a suite of causal inference techniques now available. Authors Hugo Bowne-Anderson, a data science consultant, and Mike Loukides, vice president of content strategy at O'Reilly Media, introduce causality and discuss randomized control trials (RCTs), key aspects of causal graph theory, and well-needed techniques from econometrics. | ||
650 | 0 | |a Estimation theory | |
650 | 0 | |a Conditional expectations (Mathematics) | |
650 | 0 | |a Effect sizes (Statistics) | |
650 | 0 | |a Acyclic models | |
650 | 0 | |a Causation |x Mathematical models | |
650 | 0 | |a Inference |x Mathematical models | |
650 | 0 | |a R (Computer program language) | |
650 | 4 | |a Théorie de l'estimation | |
650 | 4 | |a Espérances conditionnelles (Mathématiques) | |
650 | 4 | |a Ampleur de l'effet (Statistique) | |
650 | 4 | |a Modèles acycliques | |
650 | 4 | |a Inférence (Logique) ; Modèles mathématiques | |
650 | 4 | |a R (Langage de programmation) | |
650 | 4 | |a Acyclic models | |
650 | 4 | |a Causation ; Mathematical models | |
650 | 4 | |a Conditional expectations (Mathematics) | |
650 | 4 | |a Effect sizes (Statistics) | |
650 | 4 | |a Estimation theory | |
650 | 4 | |a Inference ; Mathematical models | |
650 | 4 | |a R (Computer program language) | |
700 | 1 | |a Loukides, Mike |e VerfasserIn |4 aut | |
710 | 2 | |a O'Reilly for Higher Education (Firm), |e MitwirkendeR |4 ctb | |
710 | 2 | |a Safari, an O'Reilly Media Company. |e MitwirkendeR |4 ctb | |
966 | 4 | 0 | |l DE-91 |p ZDB-30-ORH |q TUM_PDA_ORH |u https://learning.oreilly.com/library/view/-/9781098118990/?ar |m X:ORHE |x Aggregator |z lizenzpflichtig |3 Volltext |
912 | |a ZDB-30-ORH | ||
912 | |a ZDB-30-ORH | ||
951 | |a BO | ||
912 | |a ZDB-30-ORH | ||
049 | |a DE-91 |
Datensatz im Suchindex
DE-BY-TUM_katkey | ZDB-30-ORH-076599329 |
---|---|
_version_ | 1821494824453275648 |
adam_text | |
any_adam_object | |
author | Bowne-Anderson, Hugo Loukides, Mike |
author_corporate | O'Reilly for Higher Education (Firm) Safari, an O'Reilly Media Company |
author_corporate_role | ctb ctb |
author_facet | Bowne-Anderson, Hugo Loukides, Mike O'Reilly for Higher Education (Firm) Safari, an O'Reilly Media Company |
author_role | aut aut |
author_sort | Bowne-Anderson, Hugo |
author_variant | h b a hba m l ml |
building | Verbundindex |
bvnumber | localTUM |
collection | ZDB-30-ORH |
ctrlnum | (DE-627-1)076599329 (DE-599)KEP076599329 (ORHE)9781098118990 |
dewey-full | 519.5/4 |
dewey-hundreds | 500 - Natural sciences and mathematics |
dewey-ones | 519 - Probabilities and applied mathematics |
dewey-raw | 519.5/4 |
dewey-search | 519.5/4 |
dewey-sort | 3519.5 14 |
dewey-tens | 510 - Mathematics |
discipline | Mathematik |
edition | 1st edition. |
format | Electronic eBook |
fullrecord | <?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>02967cam a22006372 4500</leader><controlfield tag="001">ZDB-30-ORH-076599329</controlfield><controlfield tag="003">DE-627-1</controlfield><controlfield tag="005">20240228121545.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">220209s2022 xx |||||o 00| ||eng c</controlfield><datafield tag="020" ind1=" " ind2=" "><subfield code="a">9781098118990</subfield><subfield code="9">978-1-0981-1899-0</subfield></datafield><datafield tag="020" ind1=" " ind2=" "><subfield code="a">1098118995</subfield><subfield code="9">1-0981-1899-5</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627-1)076599329</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)KEP076599329</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ORHE)9781098118990</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627-1)076599329</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rda</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2=" "><subfield code="a">519.5/4</subfield><subfield code="2">23</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Bowne-Anderson, Hugo</subfield><subfield code="e">VerfasserIn</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">What Is Causal Inference?</subfield><subfield code="c">Bowne-Anderson, Hugo</subfield></datafield><datafield tag="250" ind1=" " ind2=" "><subfield code="a">1st edition.</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="a">[Erscheinungsort nicht ermittelbar]</subfield><subfield code="b">O'Reilly Media, Inc.</subfield><subfield code="c">2022</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">1 Online-Ressource (40 Seiten)</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">Online resource; Title from title page (viewed January 25, 2022)</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Causal inference lies at the heart of our ability to understand why things happen by helping us predict the result of any action. This process is vital for businesses that aspire to turn data and information into valuable knowledge. With this report, data scientists and analysts will learn a principled way of thinking about causality using a suite of causal inference techniques now available. Authors Hugo Bowne-Anderson, a data science consultant, and Mike Loukides, vice president of content strategy at O'Reilly Media, introduce causality and discuss randomized control trials (RCTs), key aspects of causal graph theory, and well-needed techniques from econometrics.</subfield></datafield><datafield tag="650" ind1=" " ind2="0"><subfield code="a">Estimation theory</subfield></datafield><datafield tag="650" ind1=" " ind2="0"><subfield code="a">Conditional expectations (Mathematics)</subfield></datafield><datafield tag="650" ind1=" " ind2="0"><subfield code="a">Effect sizes (Statistics)</subfield></datafield><datafield tag="650" ind1=" " ind2="0"><subfield code="a">Acyclic models</subfield></datafield><datafield tag="650" ind1=" " ind2="0"><subfield code="a">Causation</subfield><subfield code="x">Mathematical models</subfield></datafield><datafield tag="650" ind1=" " ind2="0"><subfield code="a">Inference</subfield><subfield code="x">Mathematical models</subfield></datafield><datafield tag="650" ind1=" " ind2="0"><subfield code="a">R (Computer program language)</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Théorie de l'estimation</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Espérances conditionnelles (Mathématiques)</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Ampleur de l'effet (Statistique)</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Modèles acycliques</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Inférence (Logique) ; Modèles mathématiques</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">R (Langage de programmation)</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Acyclic models</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Causation ; Mathematical models</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Conditional expectations (Mathematics)</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Effect sizes (Statistics)</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Estimation theory</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Inference ; Mathematical models</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">R (Computer program language)</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Loukides, Mike</subfield><subfield code="e">VerfasserIn</subfield><subfield code="4">aut</subfield></datafield><datafield tag="710" ind1="2" ind2=" "><subfield code="a">O'Reilly for Higher Education (Firm),</subfield><subfield code="e">MitwirkendeR</subfield><subfield code="4">ctb</subfield></datafield><datafield tag="710" ind1="2" ind2=" "><subfield code="a">Safari, an O'Reilly Media Company.</subfield><subfield code="e">MitwirkendeR</subfield><subfield code="4">ctb</subfield></datafield><datafield tag="966" ind1="4" ind2="0"><subfield code="l">DE-91</subfield><subfield code="p">ZDB-30-ORH</subfield><subfield code="q">TUM_PDA_ORH</subfield><subfield code="u">https://learning.oreilly.com/library/view/-/9781098118990/?ar</subfield><subfield code="m">X:ORHE</subfield><subfield code="x">Aggregator</subfield><subfield code="z">lizenzpflichtig</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ZDB-30-ORH</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ZDB-30-ORH</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">BO</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ZDB-30-ORH</subfield></datafield><datafield tag="049" ind1=" " ind2=" "><subfield code="a">DE-91</subfield></datafield></record></collection> |
id | ZDB-30-ORH-076599329 |
illustrated | Not Illustrated |
indexdate | 2025-01-17T11:20:31Z |
institution | BVB |
isbn | 9781098118990 1098118995 |
language | English |
open_access_boolean | |
owner | DE-91 DE-BY-TUM |
owner_facet | DE-91 DE-BY-TUM |
physical | 1 Online-Ressource (40 Seiten) |
psigel | ZDB-30-ORH TUM_PDA_ORH ZDB-30-ORH |
publishDate | 2022 |
publishDateSearch | 2022 |
publishDateSort | 2022 |
publisher | O'Reilly Media, Inc. |
record_format | marc |
spelling | Bowne-Anderson, Hugo VerfasserIn aut What Is Causal Inference? Bowne-Anderson, Hugo 1st edition. [Erscheinungsort nicht ermittelbar] O'Reilly Media, Inc. 2022 1 Online-Ressource (40 Seiten) Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Online resource; Title from title page (viewed January 25, 2022) Causal inference lies at the heart of our ability to understand why things happen by helping us predict the result of any action. This process is vital for businesses that aspire to turn data and information into valuable knowledge. With this report, data scientists and analysts will learn a principled way of thinking about causality using a suite of causal inference techniques now available. Authors Hugo Bowne-Anderson, a data science consultant, and Mike Loukides, vice president of content strategy at O'Reilly Media, introduce causality and discuss randomized control trials (RCTs), key aspects of causal graph theory, and well-needed techniques from econometrics. Estimation theory Conditional expectations (Mathematics) Effect sizes (Statistics) Acyclic models Causation Mathematical models Inference Mathematical models R (Computer program language) Théorie de l'estimation Espérances conditionnelles (Mathématiques) Ampleur de l'effet (Statistique) Modèles acycliques Inférence (Logique) ; Modèles mathématiques R (Langage de programmation) Causation ; Mathematical models Inference ; Mathematical models Loukides, Mike VerfasserIn aut O'Reilly for Higher Education (Firm), MitwirkendeR ctb Safari, an O'Reilly Media Company. MitwirkendeR ctb |
spellingShingle | Bowne-Anderson, Hugo Loukides, Mike What Is Causal Inference? Estimation theory Conditional expectations (Mathematics) Effect sizes (Statistics) Acyclic models Causation Mathematical models Inference Mathematical models R (Computer program language) Théorie de l'estimation Espérances conditionnelles (Mathématiques) Ampleur de l'effet (Statistique) Modèles acycliques Inférence (Logique) ; Modèles mathématiques R (Langage de programmation) Causation ; Mathematical models Inference ; Mathematical models |
title | What Is Causal Inference? |
title_auth | What Is Causal Inference? |
title_exact_search | What Is Causal Inference? |
title_full | What Is Causal Inference? Bowne-Anderson, Hugo |
title_fullStr | What Is Causal Inference? Bowne-Anderson, Hugo |
title_full_unstemmed | What Is Causal Inference? Bowne-Anderson, Hugo |
title_short | What Is Causal Inference? |
title_sort | what is causal inference |
topic | Estimation theory Conditional expectations (Mathematics) Effect sizes (Statistics) Acyclic models Causation Mathematical models Inference Mathematical models R (Computer program language) Théorie de l'estimation Espérances conditionnelles (Mathématiques) Ampleur de l'effet (Statistique) Modèles acycliques Inférence (Logique) ; Modèles mathématiques R (Langage de programmation) Causation ; Mathematical models Inference ; Mathematical models |
topic_facet | Estimation theory Conditional expectations (Mathematics) Effect sizes (Statistics) Acyclic models Causation Mathematical models Inference Mathematical models R (Computer program language) Théorie de l'estimation Espérances conditionnelles (Mathématiques) Ampleur de l'effet (Statistique) Modèles acycliques Inférence (Logique) ; Modèles mathématiques R (Langage de programmation) Causation ; Mathematical models Inference ; Mathematical models |
work_keys_str_mv | AT bowneandersonhugo whatiscausalinference AT loukidesmike whatiscausalinference AT oreillyforhighereducationfirm whatiscausalinference AT safarianoreillymediacompany whatiscausalinference |