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--- proyectos:tfg:causalidad:causcibook [2017/12/14 09:08]
Joaquín Herrero Pintado
+++ proyectos:tfg:causalidad:causcibook [2018/05/25 12:26]
Joaquín Herrero Pintado
@@ Línea 1: / Línea 1: @@
 ====== #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 198: / Línea 198: @@
 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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