LISTSERV mailing list manager LISTSERV 15.5

Help for SOCNET Archives

SOCNET Archives

SOCNET Archives


Next Message | Previous Message
Next in Topic | Previous in Topic
Next by Same Author | Previous by Same Author
Chronologically | Most Recent First
Proportional Font | Monospaced Font


Join or Leave SOCNET
Reply | Post New Message
Search Archives

Subject: Re: he Spread of Evidence-Poor Medicine via Flawed Social-Network Analysis
From: Russell Lyons <[log in to unmask]>
Reply-To:Russell Lyons <[log in to unmask]>
Date:Mon, 27 Jun 2011 13:10:30 -0400

TEXT/PLAIN (244 lines)

*****  To join INSNA, visit  *****

Dear Tom,

Thanks for copying me on your email. I was not a member of this list and
only joined now in order to post my response to your posting. I'm sorry for
the tardy reply; I was at a conference all last week. I'm also sorry you
find my title, a play on C&F's titles, "non-charming".

On the other hand, I was glad to read that you seemed to accept the
particulars of my critique; I did not see you disagree with any of them
regarding C&F. I hope that this represents now a consensus view, so that we
can turn to the larger issues, where you did disagree with me.

Are there lessons we can draw from this episode?

The last section of my paper attempted to place in context the fact
that C&F had published significant errors in top (medical) journals. For
example, one referee wrote, "their errors are in some places so egregious
that a critique of their work cannot exist without also calling into
question the rigor of review process." I wrote, "How did these errors arise
and pass inspection?  We believe that one major reason is that, as many
before us have said, statistical assumptions are routinely made when they
are unlikely to hold." I then explicated why this occurs.

I am interested to hear if you have a different suggestion on how this
happened. I am not especially interested in how it happened to C&F as
individuals--everyone makes mistakes--, but, rather, in how the system
allowed it and whether the culture indeed enables it. Which culture?  One
referee (the one who recommended rejection for my paper) wrote, "When I
first read the C-F obesity article in the NEJM, I was astonished that the
article had passed peer review.  I am confident that the C-F work would not
have survived review in any serious statistics, biostatistics, or
econometrics journal." Thus, the enabling culture appears to be the culture
of users of statistics in the applied social sciences and medicine, not
that of methodologists.  Furthermore, I cited several people from within
the user community, such as Keynes, Summers, Blalock, and Duncan. Many more
could be cited. Although you write, "These are words of a knight riding in
shining armour high above the fray, not of somebody who honours the muddy
boots of the practical researcher," I had hoped to show that my views are
not restricted to outsiders, such as myself, but are shared by practically
minded insiders.

I will not respond to all your other points; most involve the large topic
of experiments vs. observational studies, a debate which has continued for
decades. It continues to be important, especially as the NIH prepares to
allocate large sums of money for network studies:  However, I
will have to leave my contribution mostly to the citations I gave.  You may
also be interested in

D.A. Freedman. "On types of scientific enquiry: Nine success stories in
medical research."

D.A. Freedman. "From association to causation: Some remarks on the history
of statistics." Statistical Science, vol. 14 (1999) pp. 243-258.

To the extent that my paper touched on this topic (of experiments/obs.
studies), my focus was on modeling used in observational studies. Naturally
there exist good observational studies. There are also fabulous uses of
modeling--but when the results can be tested.

Let me also clarify my thoughts regarding experiments. You wrote:

> This cannot be studied by experimental 
> assignment of ties or of exchanges alone: such a restriction would 
> amount to throwing away the child (purposeful selection of ties) with 
> the bathwater (strict requirements of causal inference).

Indeed, such restrictions would be severe. What I had in mind, however, was
to use existing real-world social ties to investigate the effect of
intervention. For example, one person could be coached on losing weight and
we could see what effect this had on his/her alters. As usual, this would
involve recruiting people to the study and randomizing some to the
treatment. Of course, it would not be blind, though perhaps there is a
clever way to disguise the goals of the study.

Looking forward to your reply,

> Dear all,

> I would like to add to this thread (although the title is not charming) 
> some of my thoughts about the issue of influence in networks and how to 
> investigate it, carrying on after Sinan's contribution. I'm sorry that 
> it has grown into an overly long discussion piece.
> Summary: to study social influence we need not only experiments but also 
> observational studies, and the possibilities are not as bleak as 
> suggested by Lyons.

> What struck me most in the paper by Lyons with the non-charming title 
> are the following two points. The argument for social influence proposed 
> by Christakis and Fowler (C&F) that earlier I used to find most 
> impressive, i.e., the greater effect of incoming than of outgoing ties, 
> was countered: the difference is not significant and there are other 
> interpretations of such a difference, if it exists; and the model used 
> for analysis is itself not coherent. This implies that C&F's claims of 
> having found evidence for social influence on several outcome variables, 
> which they already had toned down to some extent after earlier 
> criticism, have to be still further attenuated. However, they do deserve 
> a lot of credit for having put this topic on the agenda in an 
> imaginative and innovative way. Science advances through trial and error 
> and through discussion. Bravo for the imagination and braveness of Nick 
> Christakis and James Fowler.

> How people influence each other is a central issue in social network 
> analysis, as Sinan Aral writes in his contribution to this thread. Our 
> everyday experience is that social influence is a strong and basic 
> aspect of our social life. Economists have found it necessary to find 
> proof of this through experimental means, arguing (Manski) that other 
> proofs are impossible. Sociologists tend to take its existence for 
> granted and are inclined to study the "how" rather than the "whether". 
> The arguments for the confoundedness of influence and homophilous 
> selection of social influence (Shalizi & Thomas Section 2.1) seem 
> irrefutable. Studying social influence experimentally, so that homophily 
> can be ruled out by design, therefore is very important and Sinan Aral 
> has listed in his message a couple of great contributions made by him 
> and others in this domain. _However, I believe that we should not 
> restrict ourselves here to experiments._ Humans (but I do not wish to 
> exclude animals or corporate actors) are purposive, wish to influence 
> and to be influenced, and much of what we do is related to achieve 
> positions in networks that enable us to influence and to be influenced 
> in ways that seem desirable to us. Selecting our ties to others, 
> changing our behaviour, and attempting to have an influence on what 
> others do, all are inseparable parts of our daily life, and also of our 
> attempts to be who we wish to be. This cannot be studied by experimental 
> assignment of ties or of exchanges alone: such a restriction would 
> amount to throwing away the child (purposeful selection of ties) with 
> the bathwater (strict requirements of causal inference).

> The logical consequence of this is that we are stuck with imperfect 
> methods. Lyons argues as though only perfect methods are acceptable, and 
> while applauding such lofty ideals I still believe that we should accept 
> imperfection, in life as in science. Progress is made by discussion and 
> improvement of imperfections, not by their eradication.

> A weakness and limitation of the methods used by C&F for analysing 
> social influence in the Framingham data was that, to say it briefly, 
> these were methods and not generative models. Their methods had the aim 
> to be sensitive to outcomes that would be unlikely if there were no 
> influence at all (a sensitivity refuted by Lyons), but they did not 
> propose credible models expressing the operation of influence and that 
> could be used, e.g., to simulate influence processes. The telltale sign 
> that their methods did not use generative models is that in their models 
> for analysis the egos are independent, after conditioning on current and 
> lagged covariates; whereas the definition of social influence is that 
> individuals are not independent.

> Together with colleagues I have developed models for the simultaneous 
> operation of social influence and tie selection (homophilous or 
> otherwise). The best reference currently is "Dynamic networks and 
> behavior: separating selection from influence" by Christian Steglich, 
> Tom Snijders, and Michael Pearson in /Sociological Methodology/, 40 
> (2010), 329-392; the methods are implemented in the Siena software 
> (there is an extensive website These models 
> and the methods based on them indeed are not perfect, but I think they 
> help to get a better understanding of influence and selection processes, 
> and we are working on their weaknesses. They assume the availability of 
> data on networks and individual behaviour or other outcomes observed in 
> a panel design, provided that the network is not too big (a couple of 
> hundred actors, currently being extended to a couple of thousand).

> In this research we have been making claims of the kind that we aim to 
> "disentangle influence and selection", and given the results by Shalizi 
> & Thomas about the confoundedness of these two, there is the question 
> what this means and whether this aim is reasonable at all. A brief 
> summary of my position is the following. We can never exclude the 
> possibility that what seems to be social influence with respect to a 
> variable Z is the consequence of earlier homophilous choice on an 
> unobserved variable Z' that later on leads to changes in the variable Z. 
> This is a simple formulation of some of the more general and 
> mathematical results obtained by Shalizi and Thomas (section 2.1). 
> "Disentangling" selection and influence is possible only under the 
> assumption that the available observed networks and individual variables 
> contain all the variables that play a role in the causal process, and if 
> moreover a number of distributional assumptions are made (cf. the remark 
> made by Shalizi and Thomas where they refer to Steglich et al., 
> unfortunately to a preprint and not to the recently published version). 
> The sensitivity to the distributional assumptions is a serious question, 
> and this is a topic that should and will be investigated. The assumption 
> that all relevant variables are observed is always questionable, but 
> statistical inference very often is done under such assumptions. We must 
> strive after observational designs where this is, to the best of our 
> knowledge, a reasonable approximation; and we can make progress on this 
> front by what we always do as social scientists: try to find out better 
> what drives these processes, come closer to determining the type of 
> network ties and the individual variables that "really" matter and how 
> they affect one another. As the great statistician R.A. Fisher said when 
> asked how to make observational studies more likely to yield causal 
> answers (cited by Cox and Wermuth, 2004): "Make your theories 
> elaborate". Instead of "true" causality, we can obtain results about 
> time ordering: are individuals similar first, and then become tied (~ 
> homophily) or are they tied first, and become similar later (~ 
> influence)? Such results, for richer and more and more relevant 
> variables, can give important scientific advances about selection and 
> influence, based on observational studies combining rich data 
> collection, insightful theorizing, and good modelling.

> Lyons in his discussion section criticizes statistical modelling, and 
> here I find his formulations a facile attack on statistical modelling of 
> observational studies. This section does not do justice to the 
> difficulties of the topic and the possibilities to make reasonable 
> advances. He writes "Yet viewing observational data through the lens of 
> statistical modelling produces new biases, generally unknown and mostly 
> unacknowledged, lurking in mathematical thickets. .... Observational 
> studies often lead to publications whose causal conclusions contradict 
> one another or are contradicted by experiments ... this is a natural 
> consequence of poor methodology." These are words of a knight riding in 
> shining armour high above the fray, not of somebody who honours the 
> muddy boots of the practical researcher. Lyons' discussion section 
> ignores that observational studies are inevitable for many scientific 
> aims, difficult indeed, but possible as I have tried to argue above. It 
> also ignores that a lot of methodologically careful observational 
> studies have been done, as well as that collectively we learn from our 
> mistakes as long as we keep our eyes open and are not intimidated by 
> authority. Most concretely, this discussion ignores that some 
> assumptions are more important for practical applicability than others. 
> For example, in linear regression, the assumption of a continuous 
> distribution is practically totally irrelevant but theoretically 
> extremely convenient; the assumption of normal distributions is 
> unimportant; the assumption of constant residual variances is important; 
> and the assumption of independent residuals is extremely important. Such 
> distinctions are argued by robustness studies: we are worried by 
> deviations from assumptions only if they invalidate expressions of 
> uncertainty such as standard errors or posterior standard deviations, 
> type I and type II error rates, etc. Methods can make invalid 
> assumptions and still give good answers.

> /Reference additional to those mentioned earlier in the thread:
> /D.R. Cox and N. Wermuth. Causality: a statistical view. /International 
> Statistical Review /*72 (2004)*, 285--305.

>   Cheers,

> Tom

SOCNET is a service of INSNA, the professional association for social
network researchers ( To unsubscribe, send
an email message to [log in to unmask] containing the line
UNSUBSCRIBE SOCNET in the body of the message.

Back to: Top of Message | Previous Page | Main SOCNET Page



CataList Email List Search Powered by the LISTSERV Email List Manager