Agents and Causes:
Reconciling Competing Theories of Causal
Reasoning
Michael R. Waldmann
Cognitive and Decision Sciences
Department of Psychology
University of Göttingen
With: Ralf Mayrhofer
Overview
1. Causal Reasoning: Two Frameworks
• Causal Bayes Nets as Psychological Models
•
•
Overview of empirical evidence
Markov violations in causal reasoning
• Dispositional Theories
•
•
Force dynamics
Agents and patients
2. How Dispositional Intuitions Guide the
Structuring of Causal Bayes Nets
•
•
Experiments: Markov Violation
Error attribution in an extended causal Bayes net
Causal Reasoning: Two Frameworks
Causal Models: Psychological Evidence
• People are sensitive to the directionality of the causal arrow
(Waldmann & Holyoak, 1992; Waldmann, 2000, 2001)
• People estimate causal power based on covariation information, and
control for co-factors (Waldmann & Hagmayer, 2001)
• Causal Bayes nets as models of causal learning (Waldmann & Martignon, 1998)
• People (and rats) differentiate between observational and interventional
predictions (Waldmann & Hagmayer, 2005; Blaisdell, Sawa, Leising, & Waldmann, 2006)
• Counterfactual causal reasoning (Meder, Hagmayer, & Waldmann, 2008, 2009)
• Categories and concepts: The neglected direction (Waldmann & Hagmayer,
2006)
• A computational Bayesian model of diagnostic reasoning (Meder, Mayrhofer,
& Waldmann, 2009)
• Abstract knowledge about mechanisms influences the parameterization of
causal models (Waldmann, 2007)
Causal Bayes Net Research: Summary
„The Bayesian Probabilistic Causal Networks framework has
stimulated a productive research program on human inferences
on causal networks. Such inferences have clear analogues in
everyday judgments about social attributions, medical diagnosis
and treatment, legal reasoning, and in many other domains
involving causal cognition. So far,research suggests two persistent
deviations from the normative model. People‘s inferences about
one event are often inappropiately influenced by other events
that are normatively irrelevant; they are unconditionally
independent or are „screened off“ by intervening nodes. At the
same time, people‘s inferences tend to be weaker than are
warranted by the normative framework.“
Rottman, B., & Hastie, R. (2013). Reasoning about causal relationships:
Inferences on causal networks. Psychological Bulletin.
Markov Violations in Causal Reasoning
The Causal Markov Condition
Definition: Conditional upon its parents (“direct causes”) each
variable X is independent of all other variables that are not
causal descendants of X (i.e., a cause “screens off” each of its
effects from the rest of the network)
6
But recent research shows…
• Recent research shows that human reasoners do consider the
states of other effects of a target effect’s cause when
inferring from the cause to a single effect
(see Rehder & Burnett, 2005; Walsh & Sloman, 2007)
vs.
An Augmented Causal Bayes Net?
Rehder & Burnett, 2005
The Causal Markov Condition:
Psychological Evidence
• Subjects typically translate causal model instructions into
representation that on the surface violate the Markov
condition.
• Humans seem to add assumptions about hidden
mechanisms that lead to violations of screening-off, even
when the cover stories are abstract.
• It is unclear where the assumptions about hidden
structure come from. People typically have only sparse
knowledge about mechanisms (Rozenblit & Keil, 2002).
Dispositional Theories
Abstract Dispositions, Force Dynamics,
and the Distinction between Agents
and Patients
• Causation as the product of an interaction between causal
participants (agents, patients) which are endowed with
dispositions, powers, or capacities.
– e.g., Aspirin has the capacity to relieve headaches. Brains have the
capacity to be influenced by Aspirin.
• Agents (who don‘t have to be humans) are the active entities
emitting forces. Patients are the entities acted upon by the
agents. Patients more or less resist the influence of the agents.
• Intuitions about abstract properties of agents and patients may
guide causal reasoning in the absence of further mechanism
knowledge.
Wolff’s Theory of Force Dynamics
(Wolff, 2007)
Cause
Allow
(enable)
Prevent
Patient
tendency
for endstate
Affector
(i.e., agent)patient
concordance
No
No
Yes
Yes
Yes
Yes
Yes
No
No
Endstate
approached
Examples
„Winds caused the boat to heel“ (cause)
„Vitamin B allowed the body to digest“ (allow)
„Winds prevented the boat from reaching the harbor“ (prevent)
Problems
• Where does the knowledge about tendencies come from if
covariation information is excluded?
• How can predictive and diagnostic inferences within complex
causal models be explained?
• How do we know whether a causal participant plays the role
of an agent or patient?
How Dispositional Intuitions Guide the
Structuring of Causal Bayes Nets
Hypotheses
1. Both agents and patients are represented as
capacity placeholders for hidden internal
mechanisms.
2. There is a tendencyEE.g.,
to blame the agent to a
large extent for both successful and
unsuccessful causal transmissions.
3. These intuitions can be represented by
elaborating or re-parameterizing the causal
Bayes net.
Experiments: Markov Violation
An Unfamiliar Domain:
Mind Reading Aliens
(see also Steyvers et al., 2003)
POR=food
(in alien language)
Dissociating Causes and Agents
I.
Cause
Effect
Agent
Patient
EE.g.,
II.
Cause
Effect
Patient
Agent
Manipulating the Agent Role
1. Sender Condition (Cause Object as Agent)
„Gonz is capable of sending out his thoughts,
and hence transmit them into the heads of Murks, Brrrx,
and Zoohng.“
Gonz
1. Reader Condition (Effect Objects as Agents)
„Murks, Brrrx, and Zoohng are capable of reading the
thoughts of Gonz. “
Murks
Brrrx
Zoohng
Experiment 1a: Which Alien is the Cause?
(Intervention Question)
10
Probability of "POR"
9
8
Reading
7
Sending
6
5
4
3
2
1
0
Cause
Effect
Imagine „POR“ was implanted
in head of cause/effect alien.
How probable is it that the
other alien thinks of „POR“.
Experiment 1b:
Blame Attributions
100
Blame Attributions (in %)
90
80
70
60
Cause
Effect
50
40
30
20
10
0
Reading
Sending
Who is more responsible,
if cause is present and
effect absent, the cause alien
or the effect alien?
Markov Violations: Experiment 2
Instruction: 4 aliens either think of POR or not; thoughts of
pink top alien (cause) covary with thoughts of bottom aliens
(effects); aliens think of POR in 70% of their time.
?
Test Question: “Imagine
?
?
10 situations with this
configuration. In how many
instances does the right
alien think of POR?”
Predictions
Gonz
?
Murks
Sender Condition:
The pattern seems to indicate that something is wrong with Gonz‘s
capacity to send. Hence, the probability of Murks having Gonz‘s
thought should be low (i.e., strong Markov violation).
Reader Condition:
The pattern seems to indicate that something is wrong with Brrrx‘s
and Zoohng‘s capacity to read. Hence, the probability of Murks having
Gonz‘s thought should be relatively intact (i.e., weak Markov violation).
Results: Experiment 2
?
10
9
8
7
?
6
5
4
3
2
1
0
F=0
F=1
F=2
Error attribution in an extended
causal Bayes net
Error attribution in causal Bayes nets
Standard Model:
C
wC
E
Stan
15
Distinguishing between two types of error
sources
Differentiating between Cause (FC)and Effect (FE)-Based Preventers:
FE
FC
C
E
Simplified Version:
FC
C
wC
E
15
Error Attribution in a Common Cause-Model
C
FC
wC
E1
E2
…
En
• FC is an unobserved common preventer, and must be
inferred from the states of C and its effects
• When C involves the agent, the strength of FC is high (i.e.,
error is mainly attributed to C), when E involves the agent,
the strength of FC is low (hence errors are primarily
attributed to the individual effects (i.e., FE, that is, wC).
15
60
80
The strength of the FC (red
– green – blue) influences
the size of the Markov
violation (i.e., slope).
40
E3
20
E2
CC=0
CC=1
wF_C 
beta(1,1)
beta(5,1)
beta(100,1)
0
C
E1
inference rating
FC
100
Model Predictions
0
1
2
# of observed effect features being on
17
Further Predictions (1): A/B Case
?
• In the basic experiments an
asymmetry between the two states
of the cause was found
• In the absent case the cause is not
active, thus mechanism assumptions
cannot have an influence
• Prediction: When both states of the cause are described as
active, the differential assumptions about error attribution
should matter in both cases
18
Markov-Experiment A/B: Results
?
10
9
8
7
?
6
5
4
3
2
1
0
NE0
NE1
NE2
N=56
19
Further Predictions (2): Causal Chains
?
IC1
FC
IC2
FC
DC
FC
E
• If each C comes with its own FC, the
difference between reading and
sending conditions should
completely disappear in a causal
chain situation
20
Chain Experiment: Results
?
10
9
8
7
?
6
5
4
3
2
1
0
0/2
1/1
2/0
N=50
21
Summary
• Causal model instructions are typically augmented
with hidden structure.
• In the absence of specific mechanism knowledge
intuitions about abstract dispositional properties of
causal participants guide the structuring of the
models.