heterogeneous causality

7/25/2019 heterogeneous causality

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HETEROGENEOUS CAUSALITYJULIAN REISS, ERASMUS UNIVERSITY ROTTERDAM


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Overview

For a scientific approach to LCA, the notion of causationis of crucial

importance

For instance, in the CALCAS project report it says that a systems approach

means distinguishing elements, and connecting these elements bymechanisms... [A] mechanism is in the first place a causal relationship that

connects the levels of two activities

Unfortunately, there is no accepted definition of cause; rather, there is a

variety of theories of causation, each of which captures important aspects

of the notion

What I want to do here is to discuss the various approaches to causation

and eventually suggest a response to what one might call the problem of

heterogeneous causality: the fact that causal relations come in many kinds


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Regularity Accounts

Regular succession cannot be all there is to causation:

Typically, conditions require enablers, other

conditions without which the effect would not occurPreventors can almost always interferewith the

operation of the cause

Few effects can be produced by one set of

conditions only (plurality of causes)Causes are therefore (at least) INUS conditions:

ABCor DEFor GHIis always followed by P


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Regularity Accounts

Understanding causation as regular succession has a

variety of advantages:

Works for a large range of important cases

Justifes use of experimental method

Connects causality with policy

But it faces counterexamples:

Causation in singular cases

Probabilistic causation

Common-cause structures


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Regularity Accounts

Understanding causation as regular succession has a

variety of advantages:

Works for a large range of important cases

Justifes use of experimental method

Connects causality with policy

But it faces counterexamples:

Causation in singular cases


Common-cause structures

C

X

Y


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Singular causation

According to a widely held theory, the everyday

conception of causation is counterfactual: Xis a

cause of Yif and only if had Xnot been, Ywould

not have been

This is also called the but-for conception of

cause at work in the law and history

Unfortunately, it is not without problems either:

Redundant causation

Makes causation subjective


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Singular causation

According to a widely held theory, the everyday

conception of causation is counterfactual: Xis a

cause of Yif and only if had Xnot been, Ywould

not have been

This is also called the but-for conception of

cause at work in the law and history

Unfortunately, it is not without problems either:

Redundant causation

Makes causation subjective

C1

Y

C2


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Idea: causes dont make their effects inevitable but rather more

probable

Immediate problem: correlation isnt causation

Hence: look for residual correlationsonce relevant factors have

been taken into account (conditional regression coefficients)

This account, too, faces counterexamples:

It doesnt work in cases that are genuinely indeterministic

(e.g., in quantum mechanics)

When two mechanisms or processes cancel, there is

causation without correlation


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Mechanistic causation

Unfortunately, there are about as many conceptions of

mechanism as there are contributors to the literature

Important: more than just causation; continuousprocess between cause and effect

Solves common-cause problems

But: causation by omission

And: in complex situations as good as useless (what

counts is not thatthere is a mechanism but that we

pick out the relevant mechanism)


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Interventions

A final idea associated with causation is that manipulating

the cause results in a change in the effect

Helps in common-cause structures and with cancelling

mechanisms

Unfortunately (surprise, surprise!), this account, too, is

beset with problems:

Not all systems are modular (such that one can always

intervene such that only the cause is manipulated)

If interventions are not ideal, results cannot be

interpreted


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Causation useful?

If all accounts of causation fail, does that mean

that the idea is not usefulfor science?

In my view, no. The problem is not that we look forcausalrelations but that we look for thecausal

relation

Thus, there is not one causal relation but rathermany different kinds of causal relation

Doesnt this lead to confusion?


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Application matters

My answer is, again, no. Different kinds of causal relationship

have different properties; some show up in regularities, others

through a spatio-temporally continuous process, others support

interventions and so onWhat matters is that we consider those relationships that have

the properties required for the application at hand:

Prediction: regularity or probability

Explanation: mechanism

Control: stability under intervention

Strategy: use tests that are able to pick out relationships that

have the properties required by the desired application


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More matters of

applicationJohn Stuart Mill taught us that relationships are causal only when theyare unconditionally invariant (day and night example)

Weve already seen that his account is unsuccessful as a univocal

analysis of causation

Nevertheless his idea contains an important insight: regular

associations are often dependent on some underlying structure

That makes causal inference doubly difficult: not only do

confounders have to be controlled for; when exporting causal claims

beyond the test population, we need to make sure that the underlying

structure of the target population is similar enoughso that causal

inferences about the target are warranted

Alas, we use lab experiments/models because targets cannot be

investigated directly


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External validity

This is the so-called problem of external validity: do causal

claims continue to hold beyond the observed population/

structure/system?

E.g., clinical trials


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External validity



structure/system?



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External validity



structure/system?


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External validity



structure/system?


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External validity



structure/system?


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External validity



structure/system?


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Securing external

validityThere are no ready-made solutions to the problem

If two systems share all relevant causal structure, we know we

can export claimsBut this is seldom known to be the case

Mechanisms are sometimes supposed to help

The idea is that one can reduce uncertainty by starting to learn

how a cause operates; part-identity of mechanisms can makeexternal validity more likely

But this works only in so far as causes operate mechanistically


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Summary

Each of the widely held theories of causation regularity,

counterfactual, probabilistic, mechanistic, interventionist is useful

to illuminate properties causal relationships have

These properties relate to causation like symptoms to diseases:there is no unique set of symptoms that alwaysco-occurs with the

disease; and, typically, a condition is characteristic of more than

one disease

This makes causal inference, policy and planning difficult

The right attitude is to first determine what properties (orsymptoms) are important in the context of applications; and then to

establish that relationships of that kind hold, and continue to hold in

the population (or system) in which application is envisaged

heterogeneous causality

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