Diferencias

Muestra las diferencias entre dos versiones de la página.

--- proyectos:tfg:causalidad:causcibook [2017/12/12 14:06]
Joaquín Herrero Pintado
+++ proyectos:tfg:causalidad:causcibook [2018/05/28 10:40] (actual)
Joaquín Herrero Pintado [#CauSciBook]
@@ Línea 1: / Línea 1: @@
 ====== #CauSciBook ======
-Twitter hashtag: https://twitter.com/hashtag/CauSciBook
+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.
-====== Cadena de tuits =====
+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.
-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 curstiones de formato, prefiero copiar el contenido del resto de tuits para que la secuencia se siga con mayor nitidez.
+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.
-<html>
-<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>
-<script async src="https://platform.twitter.com/widgets.js" charset="utf-8"></script>
-</html>
+{{:proyectos:tfg:causalidad:what-tends-to-be-index.jpg?600|}}
 In 2016 @SDMumford & I prepared the #CauSciBook by teaching PHI302/403 at @UniNMBU with exactly this structure. 28 lectures!
@@ Línea 17: / Línea 13: @@
 It worked so well I kept the plan as the default, with possibility for minor changes. 2017 we had a day of risk with @ElenaRoccaPD.
-===== Part I =====
+===== Part I - Science and Philosophy =====
+==== Ch 1 Metascience and Better Science ====
 Ch 1 #CauSciBook: There’s philosophy in science whether we like it or not. Science rests on philosophical assumptions, incl metaphysical.
@@ Línea 29: / Línea 27: @@
 Science, unaided by philosophy, cannot decide what it is for one thing to cause something else. This goes beyond the scope of science. #CauSciBook
-#CauSciBook: Our tacit, philosophical view of the nature of causation shapes the norms that we adopt for causal science, hence practice.
+Our tacit, philosophical view of the nature of causation shapes the norms that we adopt for causal science, hence practice.
-#CauSciBook: We should not abandon causation in science. Causation is vital for science: a precondition for its very existence.
+We should not abandon causation in science. Causation is vital for science: a precondition for its very existence.
 Merely looking at physics, without its interpretation, is inconclusive about whether there is causation in science. #CauSciBook
@@ Línea 67: / Línea 65: @@
 This concludes Part I of the #CauSciBook. Ontology (nature of causation) must inform epistemology (choice of method of discovery).
-===== Part II =====
+===== Part II - Perfect Correlation =====
 Part II discusses an orthodox view that causation is conceptually and epistemologically linked to perfect correlations. #CauSciBook
+==== Ch 4 Whats In a Correlation? ====
 Ch 4 What’s in a Correlation? concerns how we separate causal from accidental correlations, while neo-Humeanism cannot. #CauSciBook
+==== Ch 5 Same Cause, Same Effect ====
 Ch 5 Same Cause, Same Effect questions that causation should be robust across all contexts, which is not supported empirically. #CauSciBook
+==== Ch 6 Under Ideal Conditions ====
 Ch 6 Under Ideal Conditions shows how causal necessitation is philosophically salvaged by stipulating ideal conditions. #CauSciBook
+==== Ch 7 One Effect, One Cause? ====
 Ch 7 One Effect, One Cause? warns against simplifying causes. Treating causes in isolation misses the importance of interaction. #CauSciBook
@@ Línea 87: / Línea 93: @@
 Unsurprisingly, also this idea about causation comes from Hume. #CauSciBook #SameCauseSameEffect
->The same cause always produce the same effect, and the same effect never arises but from the same cause. This principle we derive from experience, and is the source of most of our philosophical reasoning
+The same cause always produce the same effect, and the same effect never arises but from the same cause. This principle we derive from experience, and is the source of most of our philosophical reasoning
 Scientists have strategies to deal with less-than-perfect-regularites: exception, noise, interferer, non-respondent, outlier. #CauSciBook
@@ Línea 107: / Línea 113: @@
 It might be useful to single out of a factor as the cause of an effect, but causation is typically complex. #CauSciBook
-===== Part III =====
+===== Part III - Interference and Prevention =====
 Part III of the #CauSciBook is on causal interference and prevention, presenting an alternative to the Humean orthodoxy. #CauSciBook
+==== Ch 8 Have Your Cause and Beat It ====
 Ch 8 Have Your Cause and Beat it explains why causation is sensitive to context by introducing additive interference. #CauSciBook
+==== Ch 9 From Regularities to Tendencies ====
 Ch 9 From Regularities to Tendencies argues that we should understand causes as tendencies rather than perfect regularity. #CauSciBook
+==== Ch 10 The Modality of Causation ====
+Ch 10 The Modality of Causation: causation involves a primitive modality less than necessity & more than pure contingency. #CauSciBook
+If one is really interested in ch 9 and 10 on tendencies and the dispositional modality in #CauSciBook, read also [[https://twitter.com/hashtag/WhatTendsToBe?src=hash|#WhatTendsToBe]].
+Ch 8 #CauSciBook: Perfect regularity was never a worthy goal of a theory of causation.
+A cause could be in place and start producing its effect. But that process can be interfered with so the cause need not occur. #CauSciBook
+Since causes typically interact and produce different outcomes in different contexts, it is possible for us to intervene. #CauSciBook
+The experimental method exploits this feature of causation. By manipulation, we produce effects that wouldn’t otherwise occur. #CauSciBook
+We have 2 kinds of causal interference: subtractive (removing the cause) & additive (adding something more to the cause). #CauSciBook
+Continuing my read-through of the #CauSciBook today. Up to chapter 9: From Regularities to Tendencies. (Oh yeah, a MumJum modality!)
+'Imperfect regularities could ultimately dissolve into perfect regularities if we specified all relevant facts.' We deny this. #CauSciBook
+The search for perfect regularities is misconceived. Causation is best understood and sought in terms of tendencies. #CauSciBook
+A tendency is directed toward some effect with a certain strength. A causal tendency can thus be stronger or weaker. #CauSciBook
+Tendencies, because they are causal and disposing toward an effect, are sensitive to contextual interferers. #CauSciBook
+By a causal tendency, we do not mean a statistical incidence. These two will often differ and sometimes radically so. #CauSciBook
+We need methods designed to identify causal tendential. These cannot automatically be inferred statistically. #CauSciBook
+===== Part IV - Causal Mechanisms =====
+Part IV of #CauSciBook promotes causal theories & mechanisms as an alternative to finding causation in regularity and repetition.
+==== Ch 11 Is the Business of Science to Construct Theories? ====
+ch 11 Is the Business of Science to Construct Theories? Besides data, causal theory is needed. Otherwise we only map facts. #CauSciBook
+==== Ch 12 Is More Data Better ====
+Chapter 12, Is More Data Better?, makes a case for causal singularism, where causation happens in the concrete particular. #CauSciBook
+==== Ch 13 The Explanatory Power of Mechanisms ====
+Ch 13 The Explanatory Power of Mechanism explain why we need qualitative & mechanistic knowledge for deep causal understanding. #CauSciBook
+==== Ch 14 Digging Deeper to Find the Real Causes ====
+Ch 14 Digging Deeper to Find the Real Causes? argues against the reductive project of finding mechanisms only at lower levels. #CauSciBook
+At best, data can tell us what happened. But they cannot tell us why it happened, nor what would, could or will happen elsewhere. #CauSciBook
+Data don't explain or predict. The data themselves will be in need of causal explanation. That's where theory comes in. #CauSciBook
+Unaccompanied by a causal theory, data remain impotent, with no application beyond the particular instances in the data set. #CauSciBook
+Ch 11: Theory is not the underdog of data. #CauSciBook
+Can data ever be neutral? To even get to the point of data collection, we need to make a number of non-empirical choices. #CauSciBook
+Observation is a conscious activity, not input/output. ‘People, not their eyes, see. Cameras, and eye-balls, are blind’ Hanson 1958 #CauSciBook
+Causal singularism challenges the norm that more data is always better for finding and understanding causation. #CauSciBook
+Should we have to deny Big Bang as a cause unless the same happened again many times? Hume said yes. We say no. #CauSciBook
+A causal set-up can have a unique set of properties & causal powers, which challenges the Humean requirement of repetition. #CauSciBook
+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!

filosofias.es

Herramientas de usuario

Herramientas del sitio

Diferencias

Herramientas de la página