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--- proyectos:tfg:causalidad:causcibook [2017/12/14 09:08]
Joaquín Herrero Pintado
+++ 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.
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 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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 Ch 13 on causal mechanisms. This is the stuff that scientific theories are made of: the what, the how and the why. #CauSciBook
+To learn about causal mechanisms, qualitative approaches in research are necessary. #CauSciBook
+We think of something as a cause because it makes a difference. This is a reliable but not a perfect symptom of causation. #CauSciBook
+Randomised controlled trials rely on causes to make a difference, but not all difference-makers are causes or vice versa. #CauSciBook
+A placebo group is methodologically useful for discovering whether an intervention worked but is not constitutive of it working. #CauSciBook. This point was first offered us by @RogerKerry1 and inspired the methodological part of the @Cause_Health project.
+A drug doing its causal work on those in the treatment group is unaffected by anything going on with other people. #CauSciBook
+Causal discovery cannot only be targeted of events or change. Some causation produces non-change, or stability. #CauSciBook
+===== Part V  =====
+Part V is called Linking Causes to Effects, and looks at what exactly the causal link consists in. Difference-making? Determinism? #CauSciBook
+Ch 15 Making a Difference: Counterfactual theory fails for some instances of causation & defines some non-causes as causes. #CauSciBook
+This shows that causation is not the same as difference-making; a problem for scientific methods that depend on comparisons. #CauSciBook
+Ch 16 Making Nothing Happen: some of the most important causal situations involve no change or events. #CauSciBook
+Ch 17 It All Started With a Big Bang asks whether causation is deterministic and transitive. The answer is no. #CauSciBook
+Ch 18 Does Science Need Laws of Nature? No need for universal, governing laws in addition to intrinsic propensities & their interactions. #CauSciBook
+===== Part VI =====
+Part VI is on probability. Chapter 19 Uncertainty, Certainty and Beyond is on probability as credence, or subjective belief. #CauSciBook
+We distinguish between the classical mathematical conception of probability and natural probabilities needed for causation. #CauSciBook
+Assumption that the natural world behaves according to classical probability can give us a misleading image of causation. #CauSciBook
+Ch 20 discusses probability as a worldly phenomenon, offering a distinctive account of propensities against frequentism. #CauSciBook
+Ch 21 shows how our account of natural propensities requires revisions to the orthodox treatment of conditional probability. #CauSciBook
+Beliefs should be measured on an unbounded scale (no upper or lower limit), not on the bounded scale of classical probabilities. #CauSciBook
+Ch 20 What Probabilistic Causation Should Be proposes a propensity theory of chance, but one unlike Popper or Mellor. #CauSciBook
+Would anyone here think of Cartwright or Anscombe as propensity theorists? I think they should be. #philsci
+Ch 21 Calculating Conditional Probability? We cannot escape conditionals when estimating probabilities. #CauSciBook
+Although we might speak of a probability as absolute, this does not mean that no conditions are assumed in the estimate. #CauSciBook
+It is crucial that we have scientific tools to deal with probabilities conditionally. The ratio formula fails as such a tool. #CauSciBook
+The ratio formula is the standard interpretation of conditional probability, but it is not the same as conditional probability. #CauSciBook
+Some interpretations treat conditional probability as primitive, not calculable from unconditional probabilities. We agree. #CauSciBook
+We distinguish between the tool (heuristics), the results it generates (epistemology) and the phenomenon we study (ontology). #CauSciBook
+The source of the problem lies not in the understanding of probabilities but in how conditional & causal relations are analysed. #CauSciBook
+We argue that conditional probabilities are primitive because conditionals and causal relations are primitive. #CauSciBook
+There are no conditionals in the ratio formula: no given, if, conditions, outcomes, effect, results or even probability. #CauSciBook
+We here side with Cartwright: no causation in, no causation out. The same can be said about conditionals. #CauSciBook
+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
+Ch 23: Explains why we cannot trust RCTs to offer the full causal story or be sufficient for making fully-informed decisions. #CauSciBook
+If an intervention poses a severe risk on the participants, one cannot run an RCT to test it. Other methods must be used. #CauSciBook
+RCTs cannot be tested on risk groups, in danger of getting severe effects from an intervention: children, pregnant, sick, old, etc. #CauSciBook
+Although RCTs include individual variations in their study design, this is not what the test is designed to show. #CauSciBook
+Individual propensities naturally fall outside the scope of RCTs, since all they show is what happens on group level. #CauSciBook
+Biases: health benefits of pharmaceutical interventions are easier to test & control in RCTs than social or psychological factors. #CauSciBook
+Being explicit about what is excluded from an RCT, allows a more realistic interpretation of the results - and better decisions. #CauSciBook
+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
+Ch 25 Uncovering Causal Powers offers an account of technological innovation, where teasing out hidden powers of things is crucial. #CauSciBook
+Ch 26 Learning from Causal Failure shows how new causal knowledge can arise from unsuccessful experiments and discrepancies. #CauSciBook
+Given the diminishing return in confirming evidence, after a point, breakthroughs are more likely to follow from negative results. #CauSciBook
+Ch 27 Plural Methods, One Causation argues for epistemic pluralism (many methods) combined with ontological monism (one causation). #CauSciBook
+Causation is one thing but primitive. We must then investigate it through its symptoms and our methods must detect those. #CauSciBook
+Ch 28 Getting Real about the Ideals of Science: We cannot deal with the messy reality via idealisation & abstraction. #CauSciBook
+The reproducibility crisis shows how some of our expectations of science are unrealistic, based on a mistaken notion of causation. #CauSciBook
+No experiment has ever been made that is free of presuppositions so our best practice is to acknowledge what they are. #CauSciBook
+We are causal agents and patients. Observations and theories are a mutual manifestation between the world and ourselves. #CauSciBook
+Causation is crucial for technology. Once we understand the causal powers of things, we can harness them in new technologies. #CauSciBook
+Things have more than one causal power. In designing technology for one kind of effect, we cannot ignore potentially harmful effects. #CauSciBook
+Identification of the causal powers of things remains one of the most important of tasks of technology. #CauSciBook
+Deep theoretical knowledge cannot progress simply by accumulating positive test results for our causal hypotheses. #CauSciBook
+We should recognise the rich potential to expand knowledge from cases of causal failure; to understand how or why A causes B. #CauSciBook
+There is a confirmation bias in science, to find confirmation of a theory more than is rationally warranted. This is well known. #CauSciBook
+There is also a meta-philosophical bias to adopt scientific methodology based on verification through repeated confirmation. #CauSciBook
+Evidence, like powers, can overdispose. It can get to the point where more evidence becomes epistemically redundant. #CauSciBook
+Acknowledging that causation is complex, there should be more to the causal story of B than the fact that it was preceded by an A. #CauSciBook
+Repeated positive results tell us little about complexity. Scientific progress often happens with surprising, unpredicted results. #CauSciBook
+For an example of causal insights from failure in cancer research, see @ElenaRoccaPD Rocca 2016: http://onlinelibrary.wiley.com/doi/10.1111/jep.12622/abstract (Bridging the boundaries between scientists and clinicians—mechanistic hypotheses and patient stories in risk assessment of drugs) #CauSciBook
+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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