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Causality must be defined as a fully-observable model and then proved by a replicable experiment, it cannot be established by any number of mere observations. S

by lngnmn 10y ago

Causality must be defined as a fully-observable model and then proved by a replicable experiment, it cannot be established by any number of mere observations. Statistics or probability are unrelated to causality in principle.

randcraw 10y ago
"Causal inference in statistics: An overview", Judea Pearl http://ftp.cs.ucla.edu/pub/stat_ser/r350.pdf http://ftp.cs.ucla.edu/pub/stat_ser/r350.pdf "If correlation doesn’t imply causation, then what does?", Michael Nielsen http://www.michaelnielsen.org/ddi/if-correlation-doesnt-imply-causation-then-what-does/ http://www.michaelnielsen.org/ddi/if-correlation-doesnt-impl...
- lngnmn 10y ago
  > If correlation doesn’t imply causation, then what does? Experiment. Prove of implementation, the way molecular biologists do it.
  - pc2g4d 10y ago
    The answer is more complicated than that. Pearl has shown that it some situations it's possible to answer causal questions without running experiments. Basically, you can ask causal questions using the "do-calculus" he developed: P(Dog barks | do(kick the dog) ) The `do` modifies your model to treat "kick the dog" as observed, breaking its dependence on any other variables, e.g. the doorbell ringing or the cat hissing. With those links broken, the model becomes simpler, and often the causal question can be answered using observational rather than experimental quantities. At least, that's the idea. Not sure how broadly accepted it is, but Pearl is a towering figure and his work seemed robust at least to my not-so-expert eyes.
    - lngnmn 10y ago
      That would still be a strong correlation, but not causation. One could add a weight to each observable state and select for the biggest weight so far, but it would have nothing to do with the actual causes. Would a dead dog bark when kicked? There are some principles which cannot be undone by any amount of hipsterism and sophisticated sectarian bullshitting. There is no way to jump from observation to causality without knowing the implementation. At least in this particular universe. In the realm of models (or ideas) it could be seem doable, but a map is not a territory, model does not represent reality until proven experimentally.
      - alexmlamb2 10y ago
        Well, I'd agree that you probably can't go from correlation to causation without introducing assumptions - but there are situations where you can try to make assumptions. For example, in the social sciences it's common to look for "instrument variables" which are random and can't be affected by anything else. If you can find these types of variables then you can pretend that you're looking at the results from an experiment. There are also methods like discontinuity analysis, which argue that if your system is discontinuously effected by a variable, but the causes will effect the variable in a continuous way, then you can look at changes around that specific point as being causal.
      - webreac 10y ago
        If the experiment you need to prove a model is an experiment that occurs naturally without intervention, you can prove a model just with observation.
        lngnmn 10y ago
        An experiment must be carefully designed according to the working hypothesis and control. Natural phenomena is not a formal experiment.
        rahimnathwani 10y ago
        Are you saying that it's impossible to draw conclusions from natural experiments? The 'family size' example here is nice: https://en.m.wikipedia.org/wiki/Natural_experiment#Recent_examples https://en.m.wikipedia.org/wiki/Natural_experiment#Recent_ex... I don't see why you need to design a new experiment if data that satisfy your requirements already exist. For example, if I wanted to know whether people given the name 'george' are more or less likely than the population as a whole to have at least one child, must I recruit pregnant women, and assign half of them to name their child 'george' and allow the others to choose any name? Would it not be better to look at existing data to compare Georges and non-Georges, perhaps slicing by socioeconomic variables? The results would be instant (rather than requiring 65 years to gather) and the dataset larger.
  - jessriedel 10y ago
    The key input for demonstrating causality is statistical independence, not human involvement. If there is an independent (exogenous) variable influencing the system, these can function fine ("natural experiment").
  - Gatsky 10y ago
    Except that molecular biology is a perfect example of how causal inference in complex systems fails. Hidden context, uncontrolled experimental variables, pleotropic effects, experimental noise all conspire to produce conclusions that cannot be reproduced or if reproduced, cannot be generalised. The contrast with physics is stark - theoretical physicists predict experimental results (eg Higgs boson) many years before the experiment is performed. I don't think that in biology, we merely need to prove a succession of hypotheses to achieve the kind of explanatory causal models we want. A theoretical framework is needed, and at the moment, there is not even an outline of such a framework.
- fnl 10y ago
  But the whole argument in "casual calculus" is based on the assumption that you have a situation where controlled, randomized experiments are impossible. So why/how is that route not possible to design into recommender engines, churn prediction, or contextual bandits?
  - nabla9 10y ago
    The discovery of causal relationships from purely observational data is called causal discovery. It's possible to detect the direction of causality with observation with some additional assumptions that are reasonable general. For example Additive Noise Models (ANM) assume that there is an additive noise structure in observational distribution. The key assumption is that if X causes Y, the noise in X can have an effect on Y but not vice versa. Additive noise model is near 80 per cent accurate in correctly determining cause-and-effect across large number of datasets. --- Distinguishing Cause from Effect Using Observational Data: Methods and Benchmarks Joris M. Mooij, Jonas Peters, Dominik Janzing, Jakob Zscheischler, Bernhard Schölkopf; 17(32):1−102, 2016. http://jmlr.org/papers/v17/14-518.html http://jmlr.org/papers/v17/14-518.html Center for Causal Discovery web site http://www.ccd.pitt.edu/ http://www.ccd.pitt.edu/
    - lngnmn 10y ago
      > The key assumption is that if X causes Y, the noise in X can have an effect on Y but not vice versa. Fine. What if you missed a hidden variable Z which also causes Y? What if there is also X1 which causes Y when no X is present. For example, a mapping of genetic mutations to actual diseases cannot be done from purely observational data without knowing the implementation - how particular proteins interact in this or that pipeline. Possible causal relationships does not establish or prove causality itself.
      - nabla9 10y ago
        >Fine. What if you missed a hidden variable Z which also causes Y? What if there is also X1 which causes Y when no X is present. Noise going trough Z -> Y is not present in X -> Y. Detecting X1 when X is not present is trivial. Y is not present and the noise from Y is not present. There is another cause besides Y. >Possible causal relationships does not establish or prove causality itself. Noise models can establish the arrow direction with very high probability. The strength of causality and other factors are considered separately
- fnl 10y ago
  But the whole argument in "casual calculus" is based on the assumption that you have a situation where controlled, randomized experiments are impossible. So why/how is that route not possible to design into recommender engines, churn prediction, or contextual bandits?

by lngnmn 10y ago

randcraw 10y ago

"Causal inference in statistics: An overview", Judea Pearl http://ftp.cs.ucla.edu/pub/stat_ser/r350.pdf http://ftp.cs.ucla.edu/pub/stat_ser/r350.pdf "If correlation doesn’t imply causation, then what does?", Michael Nielsen http://www.michaelnielsen.org/ddi/if-correlation-doesnt-imply-causation-then-what-does/ http://www.michaelnielsen.org/ddi/if-correlation-doesnt-impl...

lngnmn 10y ago
> If correlation doesn’t imply causation, then what does? Experiment. Prove of implementation, the way molecular biologists do it.
- pc2g4d 10y ago
  The answer is more complicated than that. Pearl has shown that it some situations it's possible to answer causal questions without running experiments. Basically, you can ask causal questions using the "do-calculus" he developed: P(Dog barks | do(kick the dog) ) The `do` modifies your model to treat "kick the dog" as observed, breaking its dependence on any other variables, e.g. the doorbell ringing or the cat hissing. With those links broken, the model becomes simpler, and often the causal question can be answered using observational rather than experimental quantities. At least, that's the idea. Not sure how broadly accepted it is, but Pearl is a towering figure and his work seemed robust at least to my not-so-expert eyes.
  - lngnmn 10y ago
    That would still be a strong correlation, but not causation. One could add a weight to each observable state and select for the biggest weight so far, but it would have nothing to do with the actual causes. Would a dead dog bark when kicked? There are some principles which cannot be undone by any amount of hipsterism and sophisticated sectarian bullshitting. There is no way to jump from observation to causality without knowing the implementation. At least in this particular universe. In the realm of models (or ideas) it could be seem doable, but a map is not a territory, model does not represent reality until proven experimentally.
    - alexmlamb2 10y ago
      Well, I'd agree that you probably can't go from correlation to causation without introducing assumptions - but there are situations where you can try to make assumptions. For example, in the social sciences it's common to look for "instrument variables" which are random and can't be affected by anything else. If you can find these types of variables then you can pretend that you're looking at the results from an experiment. There are also methods like discontinuity analysis, which argue that if your system is discontinuously effected by a variable, but the causes will effect the variable in a continuous way, then you can look at changes around that specific point as being causal.
    - webreac 10y ago
      If the experiment you need to prove a model is an experiment that occurs naturally without intervention, you can prove a model just with observation.
      - lngnmn 10y ago
        An experiment must be carefully designed according to the working hypothesis and control. Natural phenomena is not a formal experiment.
        rahimnathwani 10y ago
        Are you saying that it's impossible to draw conclusions from natural experiments? The 'family size' example here is nice: https://en.m.wikipedia.org/wiki/Natural_experiment#Recent_examples https://en.m.wikipedia.org/wiki/Natural_experiment#Recent_ex... I don't see why you need to design a new experiment if data that satisfy your requirements already exist. For example, if I wanted to know whether people given the name 'george' are more or less likely than the population as a whole to have at least one child, must I recruit pregnant women, and assign half of them to name their child 'george' and allow the others to choose any name? Would it not be better to look at existing data to compare Georges and non-Georges, perhaps slicing by socioeconomic variables? The results would be instant (rather than requiring 65 years to gather) and the dataset larger.
- jessriedel 10y ago
  The key input for demonstrating causality is statistical independence, not human involvement. If there is an independent (exogenous) variable influencing the system, these can function fine ("natural experiment").
- Gatsky 10y ago
  Except that molecular biology is a perfect example of how causal inference in complex systems fails. Hidden context, uncontrolled experimental variables, pleotropic effects, experimental noise all conspire to produce conclusions that cannot be reproduced or if reproduced, cannot be generalised. The contrast with physics is stark - theoretical physicists predict experimental results (eg Higgs boson) many years before the experiment is performed. I don't think that in biology, we merely need to prove a succession of hypotheses to achieve the kind of explanatory causal models we want. A theoretical framework is needed, and at the moment, there is not even an outline of such a framework.
fnl 10y ago
But the whole argument in "casual calculus" is based on the assumption that you have a situation where controlled, randomized experiments are impossible. So why/how is that route not possible to design into recommender engines, churn prediction, or contextual bandits?
- nabla9 10y ago
  The discovery of causal relationships from purely observational data is called causal discovery. It's possible to detect the direction of causality with observation with some additional assumptions that are reasonable general. For example Additive Noise Models (ANM) assume that there is an additive noise structure in observational distribution. The key assumption is that if X causes Y, the noise in X can have an effect on Y but not vice versa. Additive noise model is near 80 per cent accurate in correctly determining cause-and-effect across large number of datasets. --- Distinguishing Cause from Effect Using Observational Data: Methods and Benchmarks Joris M. Mooij, Jonas Peters, Dominik Janzing, Jakob Zscheischler, Bernhard Schölkopf; 17(32):1−102, 2016. http://jmlr.org/papers/v17/14-518.html http://jmlr.org/papers/v17/14-518.html Center for Causal Discovery web site http://www.ccd.pitt.edu/ http://www.ccd.pitt.edu/
  - lngnmn 10y ago
    > The key assumption is that if X causes Y, the noise in X can have an effect on Y but not vice versa. Fine. What if you missed a hidden variable Z which also causes Y? What if there is also X1 which causes Y when no X is present. For example, a mapping of genetic mutations to actual diseases cannot be done from purely observational data without knowing the implementation - how particular proteins interact in this or that pipeline. Possible causal relationships does not establish or prove causality itself.
    - nabla9 10y ago
      >Fine. What if you missed a hidden variable Z which also causes Y? What if there is also X1 which causes Y when no X is present. Noise going trough Z -> Y is not present in X -> Y. Detecting X1 when X is not present is trivial. Y is not present and the noise from Y is not present. There is another cause besides Y. >Possible causal relationships does not establish or prove causality itself. Noise models can establish the arrow direction with very high probability. The strength of causality and other factors are considered separately
fnl 10y ago
But the whole argument in "casual calculus" is based on the assumption that you have a situation where controlled, randomized experiments are impossible. So why/how is that route not possible to design into recommender engines, churn prediction, or contextual bandits?