1. Betsy Sinclair University of Chicago IGNITE, PolNet 2012, Boulder
The Case for Causality
Betsy Sinclair
University of Chicago
IGNITE, PolNet 2012, Boulder

2. The Political Networks Community...
Has been extremely successful at generating new theories about the implications of social interaction
Has pioneered the collection and use of social network data
…and still has some work to do in terms of identifying cause and effect
Our subfield’s toughest critics are concerned that we confound social influence and homophily.
For theories, talk about Padgett and the Medici. For data collection, talk about James Fowler (Framingham Heart Data) and David Lazer (patterns of cell phone use). With respect to cause and effect, talk about how our toughest critics are worried about selection bias. For our subfield to survive, we need to take up these issues and take homophily seriously.

3. “If your friend jumped off a bridge, would you jump too
“If your friend jumped off a bridge, would you jump too?” (Shalizi and Thomas 2011)
Homophily and social influence are typically confounded.
This problem is further complicated as individuals are more likely to be influenced by similar alters.
It is well-known that omitted variables bias regression results. It’s also hard to believe that we understand the variables which drive friendship selection, yet we know that individuals _want_ homophily, so we are in a position where our critics are likely to believe that we have excluded relevant omitted variables.

4. Solutions 1. Already have the right data:
Include all appropriate control variables
Control-by-cluster
2. Generate better data:
Randomize over the network
3. Use the data you already have:
Sensitivity Analysis
Randomize: Shang and Croson for NPR experiment, Nickerson for GOTV, Sinclair/McConnell/Green for GOTV, Rogowski and Sinclair for IV estimates. Sensitivity analysis provides the threshold for how large the influence of an unobserved factor would need to be to obscure the main effect.
The goal of sensitivity analysis is to provide a sense of how large an effect an omitted variable (or variables) would have to have in order to invalidated a finding. The canonical eaxample comes from smoking and lung cancer – if cigarette smokers have 9x the risk of nonsmokers for lung cancer but only because of some as yet unknown factor X, then the proportion of smokers with factor X must be at least 9x greater than the proportion of nonsmokers with factor X.

5. The Case for Robust Inference
What if there is strong evidence for social influence?
We silence many of our critics
We can begin to theorize about the instances influence occurs
What if there is limited evidence for social influence?
This generates new theoretical avenues for us to pursue
Are people sorting themselves into social networks with higher levels of homophily then we originally suspected?
Why would individuals prefer networks with homophily, particularly if we see limited evidence of social influence?
The case for robust inference is to establish facts, so that we can return to developing new theories.