$30 off During Our Annual Pro Sale. View Details »
Speaker Deck
Features
Speaker Deck
PRO
Sign in
Sign up for free
Search
Search
統計的因果推論勉強会 第4回
Search
Hikaru Goto
August 27, 2016
Research
0
1.4k
統計的因果推論勉強会 第4回
経営学系統計学エンドユーザーのための統計的因果推論勉強会の第4回目です。
Hikaru Goto
August 27, 2016
Tweet
Share
More Decks by Hikaru Goto
See All by Hikaru Goto
統計的因果推論勉強会第5回
hikaru1122
0
2.5k
R実習 2016年9月25日
hikaru1122
1
2.6k
統計的因果推論勉強会 第3回
hikaru1122
0
2.1k
統計的因果推論勉強会 第2回
hikaru1122
0
2.1k
Other Decks in Research
See All in Research
AIスパコン「さくらONE」のLLM学習ベンチマークによる性能評価 / SAKURAONE LLM Training Benchmarking
yuukit
2
920
離散凸解析に基づく予測付き離散最適化手法 (IBIS '25)
taihei_oki
PRO
1
630
GPUを利用したStein Particle Filterによる点群6自由度モンテカルロSLAM
takuminakao
0
710
A History of Approximate Nearest Neighbor Search from an Applications Perspective
matsui_528
1
120
令和最新技術で伝統掲示板を再構築: HonoX で作る型安全なスレッドフロート型掲示板 / かろっく@calloc134 - Hono Conference 2025
calloc134
0
450
超高速データサイエンス
matsui_528
1
330
SREはサイバネティクスの夢をみるか? / Do SREs Dream of Cybernetics?
yuukit
3
260
Nullspace MPC
mizuhoaoki
1
530
さまざまなAgent FrameworkとAIエージェントの評価
ymd65536
1
370
AlphaEarth Foundations: An embedding field model for accurate and efficient global mapping from sparse label data
satai
3
600
論文紹介: ReGenesis: LLMs can Grow into Reasoning Generalists via Self-Improvement
hisaokatsumi
0
150
[論文紹介] Intuitive Fine-Tuning
ryou0634
0
160
Featured
See All Featured
From π to Pie charts
rasagy
0
92
Reflections from 52 weeks, 52 projects
jeffersonlam
355
21k
Speed Design
sergeychernyshev
33
1.4k
Ecommerce SEO: The Keys for Success Now & Beyond - #SERPConf2024
aleyda
1
1.7k
[RailsConf 2023 Opening Keynote] The Magic of Rails
eileencodes
31
9.8k
Leading Effective Engineering Teams in the AI Era
addyosmani
9
1.4k
Optimizing for Happiness
mojombo
379
70k
How to audit for AI Accessibility on your Front & Back End
davetheseo
0
120
Chrome DevTools: State of the Union 2024 - Debugging React & Beyond
addyosmani
9
1k
Claude Code どこまでも/ Claude Code Everywhere
nwiizo
61
50k
Mobile First: as difficult as doing things right
swwweet
225
10k
Max Prin - Stacking Signals: How International SEO Comes Together (And Falls Apart)
techseoconnect
PRO
0
51
Transcript
ܦӦֶܥ ౷ܭֶΤϯυϢʔβʔͷͨΊͷ ౷ܭతҼՌਪษڧձ ୈ4ճ 20168݄27 @hikaru1122 1
ษڧձͷϞοτʔ • ʮհೖʯ͍ͨ͠ͳΒɺҼՌਪͷํ๏Λʹ͚ͭΑ͏ɻ • ҼՌޮՌΛਪఆ͢Δํ๏ΛֶͿɻ • ֶతͳ͜ͱʹߦ͔ͳ͍ɻ πʔϧͱͯ͠ʹ͚ͭΔɻ 2
ຊͷൣғ • ٶຊɹୈ4ষɹ53ʙ73ท • ຊɹୈ3ষʮIPW ਪఆྔʯ 69ʙ74ท 3
෮श • ࣄ࣮, ճؼੳɼҼՌޮՌɼަབྷɼڞมྔɼڧ͘ແࢹͰ͖Δ ׂΓͯ݅ɼείΞ • ాʢ2014ʣͷୈ16ষʙ17ষ෮शʹͳΔɻ 4
ٶຊɹୈ4ষ • ٶຊͷωλຊͷஶऀʢJudea Pearlʣɻ • 4ষ5ষͷཧతͳ४උͩͱࢥ͏ɻࢲ ͨͪʹେ͖ؔ͘͠ͳ͍ɻ • ͍ͬͯ͏͔ɼαούϦΘ͔Γ·ͤΜɻ 5
Θʔ͍ • ֶతʹ͓ख্͛ɻ • ·ͨڭ͑Λ͍ʹߦ͖·͢ʢʼU༷ʣɻ 6
ٶຊͷ༻ޠͷ֬ೝ • DAG ͰҼՌతҙຯ͚͕ͮՄೳͳͷΛҼՌμΠΞάϥϜͱݺͿ ʢ75ทʣɻ • DAG ͦͷͷ७ਮͳ֬Ϟσϧʢ62ทʣɻ • Ϛϧίϑੑʹۄಥ͖ͷΠϝʔδɻ
લͷঢ়ଶͰ࣍ͷঢ়ଶ͕ܾ·Δʢ;ʔΜʣɻ • ͖݅ಠཱ 7
ͳΜͰ͖݅ಠཱ͕େͳͷʁ • Γ͍ͨͷݪҼม͕݁Ռมʹ༩͑ΔӨڹɻ • ݪҼม݁ՌมʹӨڹΛ͋ͨ͑ΔͷʢަབྷҼࢠʣͷӨڹ ΛऔΓআ͍ͯɼόΠΞεͳ͘ҼՌޮՌΛਪఆ͍ͨ͠ɻ • ަབྷҼࢠͷӨڹΛίϯτϩʔϧͯ͠ɼͦΕͰͳ͓ҼՌޮՌ͕ ೝΊΒΕΔ͔Ͳ͏͔ΛΓ͍ͨɻ 8
ͦ͜ͰࠓίϨɻ • Pearl, Glymour and Jewell(2016) • ҼՌϞσϧσʔλ͕ੜ͞ΕΔϝΧ χζϜͰ͋Δɻ 9
ҼՌμΠΞάϥϜͷجຊ3ύλʔϯ • ࿈ʢchainʣɼذʢforkʣɼ߹ྲྀʢcolliderʣ • ͲΕ͕ͲΕʹӨڹΛ༩͍͑ͯΔ͔ΠϝʔδͰ͖ΕOKͰɻ • ࣍ճʢٶຊ ୈ5ষʣͷཧղʹͱͯେࣄ 10
࿈ʢchainʣ • Xֶ͕ߍͷࢿۚɼY͕ςετͷɼZ͕߹֨ • YΛҰఆͳʹ੍ݶͰ͖ΔͳΒɼXͱZͲΜͳΛͱͬͯ OKʢ͖݅ಠཱʣ 11
ذʢforkʣ • X͕ؾԹɼY͕ΞΠεΫϦʔϜച্ɼZ͕൜ࡑ • YͱZ͚ͩݟͨΒɼ૬͕ؔ͋ΔͧɻY͔ΒZͷҼՌޮՌ͋Δͷ͔ ͳʁ 12
ذʢforkʣ • YͱZʹٖ૬ؔͷڪΕ͕͋Δɻ • XΛ੍ݶͯ͠ɼYͱZͷ૬ؔΛݟΕΑ͍ɻ • ͏͜ͷखͷେৎͰ͢Ͷʂ 13
߹ྲྀʢcolliderʣ • Pearl͞ΜΒʹΑΔͱͱͬͯେࣄͳܗΒ͍͠ʢextremely important to the study of causalityʣ 14
߹ྲྀʢcolliderʣ • ͢Ͱʹࢲͨͪ͜ͷة͏͞Λ͍ͬͯ·͢ʢલճͬͨʣɻ • ZΛҰఆʹͨ͠ΒɼXͱYʹ૬͕ؔੜͯ͡͠·͏ɻ 15
ࠓͷٶຊ·ͱΊ • ҼՌͷߏΛਤͰॻ͘ͱɼ͝རӹ͋Δ ͔Αɻ • ෮शͱͯ͠ɼؠσʔλαΠΤϯεୈ3 רͷ28ʙ38ทʢྛɾࠇ 2016ʣ͕ײ ಈతʹΘ͔Γ͍͢ɻ •
͞Βʹ39ʙ46ทΛಡΜͰ͓͘ͱɼ࣍ճ ͷେࣄͳͱ͜Ζ͕ཧղͰ͖ΔʢͨͿ Μʣɻ 16
ຊɹୈ3ষʮIPWਪఆྔʯ • ΑΓΑ͘ҼՌޮՌͷਪఆΛܭࢉͰ͖Δํ๏ɻ • ࠓ࣍ͷެ͚ࣜͩͰOKɻ ɹ,ɹ • 2ͭΛҾ͖ࢉ͢ΕΑ͍ɻ 17
Ͳ͜Λܭࢉ͍ͯ͠Δͷ͔ʁ • ATEɿʢᶃʴᶄʣͷฏۉ ʔʢᶅʴᶆʣͷฏۉ • ATTͷ߹ผͷࣜʹͳΔɻ • ͜ΕҎ্ઌʢDRਪఆྔʣʹਐΈ·ͤΜɻ 18
IPWਪఆྔɹܭࢉͷ࣮ࡍ • ʮҼՌޮՌͷਪఆʂRͰ࣮ફ - είΞɼϚονϯάɼIPW ਪఆྔ -ʯ • ʮؠDS3αϙʔτϖʔδʯ •
ʮؠσʔλαΠΤϯεvol.3ͷσʔλͰ༡΅͏ʯ • 3ͭΊ͔ͳΓRʹ׳Εͯͳ͍ͱ͍͠ɻ • 1ͭΊͱ2ͭΊ͕ཧղͰ͖ΔΑ͏ʹͳΖ͏ɻ 19
IPWਪఆྔΛ༻͍ͨจ • ຊޠͰগͳ͍ɻӳޠͰະௐࠪɻ • ࠓ࣍ͷ2ຊɻ • ಛఆอ݈ࢦಋͷ༧հೖࢪࡦͷޮՌʹؔ͢ΔݚڀʢੴΒ 2013ʣ • ࣾձతݽཱͱϥΠϑΠϕϯτͷؔ࿈ʢࡾ୩
2015ʣ 20
ੴΒʢ2013ʣ SAS༻ • ʮϝλϘରࡦʢ݈ͱอ݈ࢦಋʣͬͯΈͨʯ • ʮड͚ͨਓͱड͚ͳ͔ͬͨਓͷҧ͍Λݟ͍ͨʯ • ʮ͍Ζ͍ΖվળͰ͖ͨΑʂʯ • ड͚ͨਓ924ਓɼड͚ͳ͔ͬͨਓ3128ਓ
• ϚονϯάΛͨ͠Βଟ͘ͷσʔλΛࣺͯΔ͜ͱʹͳΔͷͰIPWਪ ఆྔΛ༻͍ͨͷ͔ͳʁ 21
ࡾ୩ʢ2015ʣ ༻ιϑτෆ໌ • ʮࣾձతݽཱʹͲͷΑ͏ͳϥΠϑΠϕϯτ͕ؔ࿈͔ͨ͠ΛΓ ͍ͨʯ • ʮWebௐࠪैདྷͷํ๏ΑΓόΠΞε͕͋Δ͔Βௐ͍ͨ͠ʯ • ʮͦ͜Ͱٯ֬ॏΈ͚๏Λ͓͏ʂʯ •
ͻΐͬͱͯ͠ɼຊୈ6ষͷ༰Ͱʂʁ 22
JGSS 23
JGSS 24
࣍ճ༧ࠂ • ٶຊ ୈ5ষɹόοΫυΞج४ • ຊ ୈ4ষɹ4.1ʙ4.3 ͱ 4.7 •
ؠDS vol.3 ͷ28ʙ48ทɼ62ʙ90ท͕ಡΈ͍͢Ͱ͢ɻ • ςΫχΧϧͳʹ໎͍ࠐΉҰาखલ͔ʂʁ • ܦӦֶͷҰྲྀδϟʔφϧSMJʹܝࡌ͞Εͨ౷ܭతҼՌਪͷߟ ΛಡΜͰɼཱͪҐஔΛ֬ೝɻ 25
ࢀߟจݙ • Pearl, J., Glymour, M. and Jewell, N. P.
(2016). Causal Inference in Statistics: A Primer. John Wiley & Sons. • ੴળथ, ࠓҪതٱ, தඌ༟೭, ᜊ౻૱, ా٢࣏. (2013). ಛఆอ݈ࢦಋͷ༧հೖࢪࡦͷޮՌʹؔ͢Δݚڀ: େنσʔλϕʔεΛ༻ͨ͠είΞʹΑΔҼՌੳ. ް ੜͷࢦඪ, 60(5), 1-6. 26
ࢀߟจݙ • খౡོɾࢁຊক࢙(2013). ExcelͰֶͿڞࢄߏੳͱ άϥϑΟΧϧϞσϦϯά. ΦʔϜࣾ. • ྛַɾࠇֶ(2016). ૬ؔͱҼՌͱؙͱҹͷͳ͠ɼؠ σʔλαΠΤϯεɼvol.3ɼ28ʙ48.
27
ࢀߟจݙ • ྛޫ. (2012). JGSS ౷ܭੳηϛφʔ 2011-είΞɾ ΣΠςΟϯά๏Λ༻͍ΔҼՌੳ. ຊ൛૯߹తࣾձௐࠪڞ ಉݚڀڌݚڀจू,
(12), 107ʙ127. • ਸ(2010). ௐࠪ؍σʔλͷ౷ܭՊֶɹҼՌਪɾબ όΠΞεɾσʔλ༥߹. ؠॻళ. 28
ࢀߟจݙ • ਸ(2016). ౷ܭతҼՌޮՌͷجૅɼؠσʔλαΠΤϯ εɼvol.3ɼ62ʙ90. • ࡾ୩ΔΑ. (2015). ࣾձతݽཱͱϥΠϑΠϕϯτͷؔ࿈: είΞ๏ʹΑΔ
Web ௐࠪσʔλੳ͔Β. ཾ୩େֶࣾձ ֶ෦لཁ= Bulletin of the Faculty of Sociology, Ryukoku University, (47), 58-69. 29
ࢀߟจݙ • ٶխາ(2004). ౷ܭతҼՌਪʔճؼੳͷ৽͍͠Έ ʔ. ேॻళ. 30