=Paper=
{{Paper
|id=Vol-1504/uai2015aci_abstract3
|storemode=property
|title=None
|pdfUrl=https://ceur-ws.org/Vol-1504/uai2015aci_abstract3.pdf
|volume=Vol-1504
}}
==None==
<pdf width="1500px">https://ceur-ws.org/Vol-1504/uai2015aci_abstract3.pdf</pdf>
<pre>
Causal and statistical inference with social network data: Massive
                 challenges and meager progress




                                         Elizabeth L. Ogburn




                                               Abstract

 Interest in and availability of social network data has led to increasing attempts to make causal and
 statistical inferences using data collected from subjects linked by social network ties. But inference
 about all kinds of estimands, from simple sample means to complicated causal peer effects, is challenging
 when only a single network of non-independent observations is available. There is a dearth of principled
 methods for dealing with the dependence that such observations can manifest. We demonstrate the
 dangerously anticonservative inference that can result from a failure to account for network dependence,
 explain why results on spatial-temporal dependence are not immediately applicable to this new setting,
 and describe a few different avenues towards valid statistical and causal inference using social network
 data.

</pre>