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--- proyectos:tfg:causalidad:causcibook [2017/12/20 12:13]
Joaquín Herrero Pintado [Ch 14 Digging Deeper to Find the Real Causes]
+++ proyectos:tfg:causalidad:causcibook [2018/05/28 10:40] (actual)
Joaquín Herrero Pintado [#CauSciBook]
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 ====== #CauSciBook ======
-Esta sección trata sobre el libro que aparecerá en 2018 [[https://ranilillanjum.wordpress.com/causation-in-science-on-the-methods-of-scientific-discovery/|Causation in Science – On the Methods of Scientific Discovery]] de Rani Lill Anjum y Stephen Mumford.
+Esta sección trata sobre el libro que aparecerá en 2018 [[https://ranilillanjum.wordpress.com/causation-in-science/|Causation in Science – On the Methods of Scientific Discovery]] de Rani Lill Anjum y Stephen Mumford.
 En el hashtag [[https://twitter.com/hashtag/CauSciBook|#CauSciBook]] de Twitter se puede encontrar información sobre el libro y la investigación de Anjum y Mumford.
@@ Línea 7: / Línea 7: @@
 La coescritora del libro explica en diciembre de 2017 en una cadena de tuits los rasgos principales del libro. Enlazo el primer tuit y, por cuestiones de formato, prefiero copiar el contenido del resto de tuits para que la secuencia se siga con mayor nitidez.
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-<blockquote class="twitter-tweet" data-lang="es"><p lang="en" dir="ltr">Here is the structure of my book with <a href="https://twitter.com/SDMumford?ref_src=twsrc%5Etfw">@SDMumford</a>: Causation in Science and the Methods of Scientific Discovery. <a href="https://twitter.com/hashtag/CauSciBook?src=hash&amp;ref_src=twsrc%5Etfw">#CauSciBook</a>. <a href="https://t.co/30hyeq9JYy">pic.twitter.com/30hyeq9JYy</a></p>&mdash; Rani Lill Anjum (@ranilillanjum) <a href="https://twitter.com/ranilillanjum/status/940153928423038976?ref_src=twsrc%5Etfw">11 de diciembre de 2017</a></blockquote>
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 In 2016 @SDMumford & I prepared the #CauSciBook by teaching PHI302/403 at @UniNMBU with exactly this structure. 28 lectures!
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 We here side with Cartwright: no causation in, no causation out. The same can be said about conditionals. #CauSciBook
-#CauSciBook on RCTs: We should base decisions on the best available evidence. But what is meant by ‘best’, ‘available’ & ‘evidence’?
+On RCTs: We should base decisions on the best available evidence. But what is meant by ‘best’, ‘available’ & ‘evidence’? #CauSciBook
 RCTs systematically fail to take into account certain types of causally important knowledge, so cannot be the gold standard. #CauSciBook
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 Being explicit about what is excluded from an RCT, allows a more realistic interpretation of the results - and better decisions. #CauSciBook
-#CauSciBook: For decisions to be based on the ‘best available evidence’, ‘evidence’ must include more than what we get from RCTs.
+For decisions to be based on the ‘best available evidence’, ‘evidence’ must include more than what we get from RCTs. #CauSciBook
 Ch 24, Getting Involved, argues that causal knowledge happens in close interaction with the world, not by distanced observation. #CauSciBook
@@ Línea 326: / Línea 322: @@
 Causal contributors and preventers are both part of the causal story, and help reveal relevant factors and their interactions. #CauSciBook
+If a drug is approved because it is repeatedly confirmed to produce the effect, we don't know the full story of how it does so. #CauSciBook
+When we learn about some unpredicted effect of the drug, we also learn more about the causal mechanisms: how it works. #CauSciBook
+Uncovering potential harms and benefits is equally important. But then we cannot test only the positive effects of interventions. #CauSciBook
+Failed prediction could also mean that there were more causal factors involved than we had taken into account in our model. #CauSciBook
+While causal models are usually about the isolated context, failure typically happens in open systems and because of interferers. #CauSciBook
+If we avoid being challenged, it prevent us from learning something new. Discrepancy experiences make us wiser. #CauSciBook
+Learning about causes in all its complexity might be an open-ended process, like the hermeneutic circle. #CauSciBook
+That concludes todays #CauSciBook tweets. Tomorrow I will read and tweet the last two chapters.
+Final two chapters of the #CauSciBook: ch. 27 Plural Methods, One Causation and ch 28 Getting Real about the Ideals of Science.
+In ch 27 we argue that causation is one single thing, but that we need many methods to uncover it, since none is perfect. #CauSciBook
+We must investigate causation through its true symptoms. Methods are suitable insofar as they latch on to the right symptoms. #CauSciBook
+Most scientific methods are thought reliable for discovering causes because they look for regularities and difference-makers. #CauSciBook
+We add that the symptoms of causation should include a.o. context sensitivity, tendencies, complexity, propensity, nonlinearity. #CauSciBook
+Evidential hierachies of scientific methods should reflect what we think is the nature of causation. #CauSciBook
+In the #CauSciBook we have shown that our understanding of causation significantly influences how science is shaped and practiced.
+The final norm of science discussed is reproducibility: that scientific findings can be independently confirmed by others. #CauSciBook
+Reproducibility relates to objectivity, reliability, repeatability, robustness, generalisability, universal application, predictability. #CauSciBook
+Reproducibility is considered a cornerstone of science, but it is a principle with strong commitments to Hume's causal theory. #CauSciBook
+Failure to reproduce is often blamed on scientists: no transparency, bias, misconduct, error, publication pressure, poor data. #CauSciBook
+We argue that the principle of reproducibility should be subject to critical scrutiny, in light of our discussion of causation. #CauSciBook
+The expectation that a study can be perfectly replicated & deliver exactly the same result, is philosophically problematic. #CauSciBook
+When a study is repeated & results diverge, there are 2 responses: there's a causally relevant difference between them or one study is flawed.
+But there's a third response to a failure to reproduce: that causation doesn't work in this way. #CauSciBook
+Reproducibility rests on 4 assumptions: same cause, same effect, causal necessitation, total cause, deterministic & closed system. #CauSciBook
+In the #CauSciBook we have challenged all 4 assumptions on philosophical grounds.
+Science, however, deals with open systems, unknown/uncertain factors, nonlinear interactions & chancy or hypersensitive elements. #CauSciBook
+Science, however, deals with open systems, unknown/uncertain factors, nonlinear interactions & chancy or hypersensitive elements. #CauSciBook
+Problem: if we don’t know which factors are causally relevant to, then everything is potentially equally important to replicate. #CauSciBook
+Perfect replication holds very little power if we are interested in robustness & generalisability of the causal insights. #CauSciBook
+Understood as perfect replicability, reproducibility works best if what we replicate is models, not real life events. #CauSciBook
+Different approaches supporting the same causal conclusion carry more epistemic weight than replication of a study. #CauSciBook
+If the theory of evolution could only be demonstrated using the same genetic string of mice in the same lab, how useful would it be? #CauSciBook
+We need new, realistic norms for science; for real people, real situations, real organisms & realistic standards for prediction. #CauSciBook
+First, we must think outside the box of idealised models where context, complexity & variation are enemies of causal knowledge. #CauSciBook
+The conclusion of the #CauSciBook is called New Norms of Science. The norms are listed in this @Cause_Health blog: [[https://causehealthblog.wordpress.com/2017/10/10/what-is-the-guidelines-challenge/|What is the Guidelines Challenge?]]
+I have now tweeted the whole #CauSciBook, Causation in Science and the Methods of Scientific Discovery. Thanks for engaging with it!

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