Knowledge progresses by finding causes of phenomena
e.g., Human activity $\Rightarrow ? \Rightarrow$ global warming
from "An Illustrated Book of Bad Arguments" by Ali Almossawi
Hypothesis Testing
A description of Theories, Phenomena, and Data (Guala, 2005)
Theories explain Phenomena
Phenomena organize data that are messy, suggestive and idiosyncratic
Data do not need a theoretical explanation, while phenomena do
Distinction between data and phenomena: two stages of scientific research
Phase 1: data are organized to identify phenomena
Phase 2: causes of a phenomena are organized into theories
Perfectly Controlled Experimental Design (PCED)
A Perfectly Controlled Experimental Design (PCED) represents in an abstract fashion the best way to test causal
relations (Guala, 2005)
A PCED is built around comparison and controlled variation
Comparison → groups that have been exposed to
different conditions are juxtaposed
Controlled variation → all factors that are not
intentionally manipulated should be kept constant across groups
(uniformity)
Controlled Variation
How to obtain a controlled variation?
Matching
Units that are identical are assigned to groups in different conditions
Consider a response variable $Y$
$Y_t(u)$ is the value of the response that would be observed
if the unit were exposed to $t$
$Y_t(u)-Y_c(u)$ is the causal effect of $t$ (relative to
$c$) on $u$
Causal inference refers to the appraisal of $Y_t(u)-Y_c(u)$
Fundamental Problem of Causal Inference: it is impossible to observe the value of $Y_t(u)$ and $Y_c(u)$ on the same unit and, therefore, it is impossible to observe the effect of $t$ on $u$
?
Controlled Variation (ii)
How to obtain a controlled variation?
2. Randomization
The random assignment of units to groups in different conditions makes the groups identical in expected terms
the mean of $Y_{i1}$ does not depend on the treatment status ($E[Y_{i1}|D_i]=E[Y_{i1}]$)
and
the mean of $Y_{i0}$ does not depend on the treatment status ($E[Y_{i0}|D_i]=E[Y_{i0}]$)
→ $ATE=ATT=E[\beta_i]$
This condition holds when individuals are randomly assigned to treatments
Being assigned to a treatment does not depend on the output variable $Y_i$
In field happenstance data, it is likely that $E[Y_{i}|D_i]\neq E[Y_{i}]$ (self selection into a treatment)
Solved econometrically via instrumental variables or other "selection on observables" techniques.
Models of Experimental Design
Basic models of experimental design (guala, 2005)
Post-test
The (causal) effect is given by $Y_{G_1}-Y_{G_2}$
Pre-test/post-test
The (causal) effect is given by $(O_{G_2}-O_{G_1})-(O_{G_4}-O_{G_3})$ (diff in diff)
The Pre-test/post-test allows to control for the impact of measurement
Experimental Economics
Why Running Economic Experiments?
Roth (1995) identifies 3 main motives for experiments in Economics
Speaking to Theorists
Testing predictions originating from formal theories
The organization of empirical regularities into formal theories
Searching for Facts (Meaning)
The study of effects that are not (yet) part of a
well-structured theory
The accumulation of facts that may lead to the creation of new theories (``Searching for Meanings'')
Whispering to the Ears of Princes
The formulation of advices for policy makers
Methodological Issues
Hertwig and Ortmann (2001) identify 4 essential methodological aspect of experiments in economics
Script Enactment
Comprehensive instructions reduce risk of ``demand effects''
Favors replicability
Repeated trials
Allows for learning and, potentially, equilibrium convergence
Performance-based monetary payments
Proper monetary payments allow to control for the disturbance due
to cognitive effort
Deception
Deception may induce suspicion among participants and
generate uncontrolled reputation spillovers
Validity of Experimental Findings
Methodological aspect affect the validity of the experiment
The validity of an experiment can be assessed along two dimensions
Internal validity
The extent to which an experiment allows to draw inferences
about the behavior in the experiment
External validity
The extent to which an experiment allows to draw inferences
about behavior outside the experiment
Internal Validity
`Just as we need to use clean test tubes in chemistry
experiments, so we need to get the laboratory conditions right
when testing economic theory'' (Binmore, 1999)
To ensure internal validity a proper experimental setting should
be implemented
Examples of dirty tubes
Bad incentive schemes
Individuals self-select into treatments
Deceitful instructions
Experimenter effects
Reputation (supergames)
External Validity
3 dimensions of artificiality that may impact on external validity of experiments (Bardsley et al., 2010)
Artificiality of isolation
The experimental setting may omit relevant features of the external environment
Findings in the lab may not survive when transferred to the
complexity of the real-world
Artificiality of omission and contamination
The laboratory environment may bias results (e.g., bad incentives)
A lack of internal validity produces a lack of external
validity
...
External Validity (ii)
3. Artificiality of alteration
The laboratory alters natural phenomena
Individuals attach a different meaning to a market in the laboratory and to a real stock market $\rightarrow$ it is impossible to study stock markets in the laboratory
Artificiality of alteration is the most challenging