Causal inference for ex-post evaluation of transportinterventions
Dan Graham
Professor of Statistical Modelling

Centre for Transport Studies
d.j.graham@imperial.ac.uk

Introduction: ex-post evaluation

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Introduction: ex-post evaluation
Paper is concerned with ex-post evaluation of transportinterventions (i.e. schemes, investments, regulations, policies etc)

1 / 16

Introduction: ex-post evaluation
Paper is concerned with ex-post evaluation of transportinterventions (i.e. schemes, investments, regulations, policies etc)
Ex-post evaluation can inform decisions on

1 / 16

Introduction: ex-post evaluation
Paper is concerned with ex-post evaluation of transportinterventions (i.e. schemes, investments, regulations, policies etc)
Ex-post evaluation can inform decisions on⋆ Value for money - study the performance of past interventions

1 / 16

Introduction: ex-post evaluation
Paper is concerned with ex-post evaluation of transportinterventions (i.e. schemes, investments, regulations, policies etc)
Ex-post evaluation can inform decisions on⋆ Value for money - study the performance of past interventions⋆ Resource allocation - forecast the impacts of future interventions
on the basis of past experience1 / 16

Introduction: ex-post evaluation
Paper is concerned with ex-post evaluation of transportinterventions (i.e. schemes, investments, regulations, policies etc)
Ex-post evaluation can inform decisions on⋆ Value for money - study the performance of past interventions⋆ Resource allocation - forecast the impacts of future interventions
on the basis of past experience

We can conduct ex-post evaluation via

1 / 16

Introduction: ex-post evaluation
Paper is concerned with ex-post evaluation of transportinterventions (i.e. schemes, investments, regulations, policies etc)
Ex-post evaluation can inform decisions on⋆ Value for money - study the performance of past interventions⋆ Resource allocation - forecast the impacts of future interventions
on the basis of past experience

We can conduct ex-post evaluation via
1. Cost Benefit Analysis (CBA) - using ‘real’ rather than predictedvalues for time saving, demand changes, prices, costs, revenues etc

1 / 16

Introduction: ex-post evaluation
Paper is concerned with ex-post evaluation of transportinterventions (i.e. schemes, investments, regulations, policies etc)
Ex-post evaluation can inform decisions on⋆ Value for money - study the performance of past interventions⋆ Resource allocation - forecast the impacts of future interventions
on the basis of past experience

We can conduct ex-post evaluation via
1. Cost Benefit Analysis (CBA) - using ‘real’ rather than predictedvalues for time saving, demand changes, prices, costs, revenues etc
2. Case studies - detailed empirical before and after comparisons forindividual schemes

1 / 16

Introduction: ex-post evaluation
Paper is concerned with ex-post evaluation of transportinterventions (i.e. schemes, investments, regulations, policies etc)
Ex-post evaluation can inform decisions on⋆ Value for money - study the performance of past interventions⋆ Resource allocation - forecast the impacts of future interventions
on the basis of past experience

We can conduct ex-post evaluation via
1. Cost Benefit Analysis (CBA) - using ‘real’ rather than predictedvalues for time saving, demand changes, prices, costs, revenues etc
2. Case studies - detailed empirical before and after comparisons forindividual schemes
3. Statistical modelling - applying statistical models to available data
to infer the causal impacts of interventions

1 / 16

Introduction: ex-post evaluation
Paper is concerned with ex-post evaluation of transportinterventions (i.e. schemes, investments, regulations, policies etc)
Ex-post evaluation can inform decisions on⋆ Value for money - study the performance of past interventions⋆ Resource allocation - forecast the impacts of future interventions
on the basis of past experience

We can conduct ex-post evaluation via
1. Cost Benefit Analysis (CBA) - using ‘real’ rather than predictedvalues for time saving, demand changes, prices, costs, revenues etc
2. Case studies - detailed empirical before and after comparisons forindividual schemes
3. Statistical modelling - applying statistical models to available data
to infer the causal impacts of interventions

Focus on statistical modelling approaches for causal inference
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Why is ‘causality’ relevant?

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Why is ‘causality’ relevant?
Transport interventions are fundamentally designed to manipulatecause-effect relationships

2 / 16

Why is ‘causality’ relevant?
Transport interventions are fundamentally designed to manipulatecause-effect relationships⋆ Fuel taxation to reduce transport emissions⋆ Congestion charging to reduce traffic volumes⋆ Road cameras to reduce speeds / mitigate accidents⋆ Infrastructure investment to boost the productivity of the economy⋆ Public transport upgrades to improve network performance2 / 16

Why is ‘causality’ relevant?
Transport interventions are fundamentally designed to manipulatecause-effect relationships⋆ Fuel taxation to reduce transport emissions⋆ Congestion charging to reduce traffic volumes⋆ Road cameras to reduce speeds / mitigate accidents⋆ Infrastructure investment to boost the productivity of the economy⋆ Public transport upgrades to improve network performance
Causality implies a comparison of outcomes under intervention relative
to some other scenario (i.e. do-nothing)2 / 16

Why is ‘causality’ relevant?
Transport interventions are fundamentally designed to manipulatecause-effect relationships⋆ Fuel taxation to reduce transport emissions⋆ Congestion charging to reduce traffic volumes⋆ Road cameras to reduce speeds / mitigate accidents⋆ Infrastructure investment to boost the productivity of the economy⋆ Public transport upgrades to improve network performance
Causality implies a comparison of outcomes under intervention relative
to some other scenario (i.e. do-nothing)
Research challenge: use observed data linking interventions withoutcomes to quantify cause-effect relationships

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Causal inference models in statistics

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Causal inference models in statistics
The need for causal data analysis across many areas of sciencemotivated the study of causal inference within statistics

3 / 16

Causal inference models in statistics
The need for causal data analysis across many areas of sciencemotivated the study of causal inference within statistics
The causal inference problem would be straightforward given either

3 / 16

Causal inference models in statistics
The need for causal data analysis across many areas of sciencemotivated the study of causal inference within statistics
The causal inference problem would be straightforward given either
1. Full data - outcomes under intervention and any otherchosen scenarios are observed allowing for direct comparison

3 / 16

Causal inference models in statistics
The need for causal data analysis across many areas of sciencemotivated the study of causal inference within statistics
The causal inference problem would be straightforward given either
1. Full data - outcomes under intervention and any otherchosen scenarios are observed allowing for direct comparison⋆ i.e. same network with and without congestion charging

3 / 16

Causal inference models in statistics
The need for causal data analysis across many areas of sciencemotivated the study of causal inference within statistics
The causal inference problem would be straightforward given either
1. Full data - outcomes under intervention and any otherchosen scenarios are observed allowing for direct comparison⋆ i.e. same network with and without congestion charging

2. Experimental evidence - random assignment of interventions
allows causal effects to be quantified by comparing averages

3 / 16

Causal inference models in statistics
The need for causal data analysis across many areas of sciencemotivated the study of causal inference within statistics
The causal inference problem would be straightforward given either
1. Full data - outcomes under intervention and any otherchosen scenarios are observed allowing for direct comparison⋆ i.e. same network with and without congestion charging

2. Experimental evidence - random assignment of interventions
allows causal effects to be quantified by comparing averages⋆ i.e. mean accidents from speed camera sites vs mean accidents fromsites without speed camera

3 / 16

Causal inference models in statistics
The need for causal data analysis across many areas of sciencemotivated the study of causal inference within statistics
The causal inference problem would be straightforward given either
1. Full data - outcomes under intervention and any otherchosen scenarios are observed allowing for direct comparison⋆ i.e. same network with and without congestion charging

2. Experimental evidence - random assignment of interventions
allows causal effects to be quantified by comparing averages⋆ i.e. mean accidents from speed camera sites vs mean accidents fromsites without speed camera

In practice the observed data typically do to fulfil these criteria:
we have incomplete data and confounding

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Statistical solutions for causal inference

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Statistical solutions for causal inference
The key objective is to capture the marginal effect of theintervention on some defined outcome, net of other influences

4 / 16

Statistical solutions for causal inference
The key objective is to capture the marginal effect of theintervention on some defined outcome, net of other influences
Paper describes various methods, and assumptions that must be
met, to draw valid casual inference

4 / 16

Statistical solutions for causal inference
The key objective is to capture the marginal effect of theintervention on some defined outcome, net of other influences
Paper describes various methods, and assumptions that must be
met, to draw valid casual inference
Statistical solutions focus on:

4 / 16

Statistical solutions for causal inference
The key objective is to capture the marginal effect of theintervention on some defined outcome, net of other influences
Paper describes various methods, and assumptions that must be
met, to draw valid casual inference
Statistical solutions focus on:
i. Imputing full data - use observed data to model counterfactual
outcomes

4 / 16

Statistical solutions for causal inference
The key objective is to capture the marginal effect of theintervention on some defined outcome, net of other influences
Paper describes various methods, and assumptions that must be
met, to draw valid casual inference
Statistical solutions focus on:
i. Imputing full data - use observed data to model counterfactual
outcomesii. Simulating random treatment assignment - adjust for
confounding to mimic a pseudo-random assignment

4 / 16

Statistical solutions for causal inference
The key objective is to capture the marginal effect of theintervention on some defined outcome, net of other influences
Paper describes various methods, and assumptions that must be
met, to draw valid casual inference
Statistical solutions focus on:
i. Imputing full data - use observed data to model counterfactual
outcomesii. Simulating random treatment assignment - adjust for
confounding to mimic a pseudo-random assignment
In this talk we use two case studies to demonstrate causal inferencetechniques

4 / 16

Statistical solutions for causal inference
The key objective is to capture the marginal effect of theintervention on some defined outcome, net of other influences
Paper describes various methods, and assumptions that must be
met, to draw valid casual inference
Statistical solutions focus on:
i. Imputing full data - use observed data to model counterfactual
outcomesii. Simulating random treatment assignment - adjust for
confounding to mimic a pseudo-random assignment
In this talk we use two case studies to demonstrate causal inferencetechniques
1. Impacts of urban road network capacity expansions in the US

4 / 16

Statistical solutions for causal inference
The key objective is to capture the marginal effect of theintervention on some defined outcome, net of other influences
Paper describes various methods, and assumptions that must be
met, to draw valid casual inference
Statistical solutions focus on:
i. Imputing full data - use observed data to model counterfactual
outcomesii. Simulating random treatment assignment - adjust for
confounding to mimic a pseudo-random assignment
In this talk we use two case studies to demonstrate causal inferencetechniques
1. Impacts of urban road network capacity expansions in the US
2. Regional economic impacts of High Speed Rail investments in Spain
4 / 16Case study 1: impacts of urban road capacity expansions

TTI urban mobility data on road traffic conditions for 101 US
cities (1982-2007)

5 / 16

Background and objectives

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Background and objectives
Background: road capacity expansions have consequences for

6 / 16

Background and objectives
Background: road capacity expansions have consequences for⋆ traffic volumes (e.g. ‘induced’ demand),6 / 16

Background and objectives
Background: road capacity expansions have consequences for⋆ traffic volumes (e.g. ‘induced’ demand),⋆ network performance (e.g. speeds, travel times, flow, density etc)

6 / 16

Background and objectives
Background: road capacity expansions have consequences for⋆ traffic volumes (e.g. ‘induced’ demand),⋆ network performance (e.g. speeds, travel times, flow, density etc)
⋆ economy and productivity (e.g. GVA, wages, TFP, growth etc)

6 / 16

Background and objectives
Background: road capacity expansions have consequences for⋆ traffic volumes (e.g. ‘induced’ demand),⋆ network performance (e.g. speeds, travel times, flow, density etc)
⋆ economy and productivity (e.g. GVA, wages, TFP, growth etc)
Contribution: a statistical approach for ex-post evaluation

6 / 16

Background and objectives
Background: road capacity expansions have consequences for⋆ traffic volumes (e.g. ‘induced’ demand),⋆ network performance (e.g. speeds, travel times, flow, density etc)
⋆ economy and productivity (e.g. GVA, wages, TFP, growth etc)
Contribution: a statistical approach for ex-post evaluation⋆ provides a unified framework to assess relative effects of capacity
expansions on demand, network performance, and productivity

6 / 16

Background and objectives
Background: road capacity expansions have consequences for⋆ traffic volumes (e.g. ‘induced’ demand),⋆ network performance (e.g. speeds, travel times, flow, density etc)
⋆ economy and productivity (e.g. GVA, wages, TFP, growth etc)
Contribution: a statistical approach for ex-post evaluation⋆ provides a unified framework to assess relative effects of capacity
expansions on demand, network performance, and productivity⋆ addresses key empirical problems cited in existing work

6 / 16

Background and objectives
Background: road capacity expansions have consequences for⋆ traffic volumes (e.g. ‘induced’ demand),⋆ network performance (e.g. speeds, travel times, flow, density etc)
⋆ economy and productivity (e.g. GVA, wages, TFP, growth etc)
Contribution: a statistical approach for ex-post evaluation⋆ provides a unified framework to assess relative effects of capacity
expansions on demand, network performance, and productivity⋆ addresses key empirical problems cited in existing work
Results: quantify changes in ‘responses’ (i.e demand, performance,
productivity) caused by treatments (i.e amount of capacity expansion)
net of confounding effects

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Ex-post evaluation via causal ATE estimation

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Ex-post evaluation via causal ATE estimation
Set-up: observed data zi = (yi , di , xi ), i = 1, ..., n, where yi is a
response, di is treatment (dose), and xi is a vector of covariates

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Ex-post evaluation via causal ATE estimation
Set-up: observed data zi = (yi , di , xi ), i = 1, ..., n, where yi is a
response, di is treatment (dose), and xi is a vector of covariates
Target of inference: we want to estimate Average Treatment
Effects (ATEs)τ (d∗ ) = E [Y (d∗ )] − E [Y (0)]for all doses d∗ ∈ D ⊆ R of interest.

7 / 16

Ex-post evaluation via causal ATE estimation
Set-up: observed data zi = (yi , di , xi ), i = 1, ..., n, where yi is a
response, di is treatment (dose), and xi is a vector of covariates
Target of inference: we want to estimate Average Treatment
Effects (ATEs)τ (d∗ ) = E [Y (d∗ )] − E [Y (0)]for all doses d∗ ∈ D ⊆ R of interest.
But we have incomplete data and confounding

example7 / 16

Ex-post evaluation via causal ATE estimation
Set-up: observed data zi = (yi , di , xi ), i = 1, ..., n, where yi is a
response, di is treatment (dose), and xi is a vector of covariates
Target of inference: we want to estimate Average Treatment
Effects (ATEs)τ (d∗ ) = E [Y (d∗ )] − E [Y (0)]for all doses d∗ ∈ D ⊆ R of interest.
But we have incomplete data and confounding

exampleTo adjust for confounding we model the treatment assignment
mechanism to simulate a randomised design

7 / 16

Ex-post evaluation via causal ATE estimation
Set-up: observed data zi = (yi , di , xi ), i = 1, ..., n, where yi is a
response, di is treatment (dose), and xi is a vector of covariates
Target of inference: we want to estimate Average Treatment
Effects (ATEs)τ (d∗ ) = E [Y (d∗ )] − E [Y (0)]for all doses d∗ ∈ D ⊆ R of interest.
But we have incomplete data and confounding

exampleTo adjust for confounding we model the treatment assignment
mechanism to simulate a randomised design
This is done by calculating generalised propensity scores

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Generalised Propensity Score (GPS) adjustment

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Generalised Propensity Score (GPS) adjustment
The GPS measures the conditional probability of assignment totreatment given confoundersπ(d∗ |X; α) = Pr(D = d∗ |X = x)

8 / 16

Generalised Propensity Score (GPS) adjustment
The GPS measures the conditional probability of assignment totreatment given confoundersπ(d∗ |X; α) = Pr(D = d∗ |X = x)
Under random assignment all units have an equal probability of treatment

8 / 16

Generalised Propensity Score (GPS) adjustment
The GPS measures the conditional probability of assignment totreatment given confoundersπ(d∗ |X; α) = Pr(D = d∗ |X = x)
Under random assignment all units have an equal probability of treatment
We can use the GPS conditional probability values to create a
pseudo-sample that simulates random assignment

8 / 16

Generalised Propensity Score (GPS) adjustment
The GPS measures the conditional probability of assignment totreatment given confoundersπ(d∗ |X; α) = Pr(D = d∗ |X = x)
Under random assignment all units have an equal probability of treatment
We can use the GPS conditional probability values to create a
pseudo-sample that simulates random assignment
This is achieved via a four step approach8 / 16

Generalised Propensity Score (GPS) adjustment
The GPS measures the conditional probability of assignment totreatment given confoundersπ(d∗ |X; α) = Pr(D = d∗ |X = x)
Under random assignment all units have an equal probability of treatment
We can use the GPS conditional probability values to create a
pseudo-sample that simulates random assignment
This is achieved via a four step approachi. Estimate the GPSs using a flexible model: πb(d|X; αb)

8 / 16

Generalised Propensity Score (GPS) adjustment
The GPS measures the conditional probability of assignment totreatment given confoundersπ(d∗ |X; α) = Pr(D = d∗ |X = x)
Under random assignment all units have an equal probability of treatment
We can use the GPS conditional probability values to create a
pseudo-sample that simulates random assignment
This is achieved via a four step approachi. Estimate the GPSs using a flexible model: πb(d|X; αb)ii. Adjust for confounding via a mean response model: E [Y |D, πb (d|X; αb); β]

8 / 16

Generalised Propensity Score (GPS) adjustment
The GPS measures the conditional probability of assignment totreatment given confoundersπ(d∗ |X; α) = Pr(D = d∗ |X = x)
Under random assignment all units have an equal probability of treatment
We can use the GPS conditional probability values to create a
pseudo-sample that simulates random assignment
This is achieved via a four step approachi. Estimate the GPSs using a flexible model: πb(d|X; αb)ii. Adjust for confounding via a mean response model: E [Y |D, πb (d|X; αb); β]

iii. Use βb to calculate expected response at dose d∗hiµb(d∗ ) = E[Y (d∗ )] = EX E(Y |d∗ , πb (d∗ |X; αb)); βb ,and repeat for all doses of interest.

8 / 16

Generalised Propensity Score (GPS) adjustment
The GPS measures the conditional probability of assignment totreatment given confoundersπ(d∗ |X; α) = Pr(D = d∗ |X = x)
Under random assignment all units have an equal probability of treatment
We can use the GPS conditional probability values to create a
pseudo-sample that simulates random assignment
This is achieved via a four step approachi. Estimate the GPSs using a flexible model: πb(d|X; αb)ii. Adjust for confounding via a mean response model: E [Y |D, πb (d|X; αb); β]

iii. Use βb to calculate expected response at dose d∗hiµb(d∗ ) = E[Y (d∗ )] = EX E(Y |d∗ , πb (d∗ |X; αb)); βb ,and repeat for all doses of interest.

iv. Calculate ATEs: τb(d∗ ) = µb(d∗ ) − µb(0), using (block) bootstrap forvariance estimation
8 / 16

Advantages of the causal GPS approach

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Advantages of the causal GPS approach⋆ Clearly defined ‘causal’ framework based on 3 key empiricalcomponents: response, treatment, confounders

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Advantages of the causal GPS approach⋆ Clearly defined ‘causal’ framework based on 3 key empiricalcomponents: response, treatment, confounders⋆ Circumvents need for a comprehensive theoretical model, though
theory informs selection of confounders

9 / 16

Advantages of the causal GPS approach⋆ Clearly defined ‘causal’ framework based on 3 key empiricalcomponents: response, treatment, confounders⋆ Circumvents need for a comprehensive theoretical model, though
theory informs selection of confounders⋆ Approach can estimate ATEs across multiple doses rather than asingle point estimate

9 / 16

Advantages of the causal GPS approach⋆ Clearly defined ‘causal’ framework based on 3 key empiricalcomponents: response, treatment, confounders⋆ Circumvents need for a comprehensive theoretical model, though
theory informs selection of confounders⋆ Approach can estimate ATEs across multiple doses rather than asingle point estimate⋆ Modelling with a scalar PS, rather than high-dimensional X, allows
use of flexible forms (i.e. GAMs and high-order polynomial)

9 / 16

Advantages of the causal GPS approach⋆ Clearly defined ‘causal’ framework based on 3 key empiricalcomponents: response, treatment, confounders⋆ Circumvents need for a comprehensive theoretical model, though
theory informs selection of confounders⋆ Approach can estimate ATEs across multiple doses rather than asingle point estimate⋆ Modelling with a scalar PS, rather than high-dimensional X, allows
use of flexible forms (i.e. GAMs and high-order polynomial)⋆ GPS can be used to form a number of ATE estimators via
weighting, matching, or regression (combine for doubly robust)

9 / 16

Advantages of the causal GPS approach⋆ Clearly defined ‘causal’ framework based on 3 key empiricalcomponents: response, treatment, confounders⋆ Circumvents need for a comprehensive theoretical model, though
theory informs selection of confounders⋆ Approach can estimate ATEs across multiple doses rather than asingle point estimate⋆ Modelling with a scalar PS, rather than high-dimensional X, allows
use of flexible forms (i.e. GAMs and high-order polynomial)⋆ GPS can be used to form a number of ATE estimators via
weighting, matching, or regression (combine for doubly robust)⋆ A longitudinal mixed model extension of the GPS can addressunobserved effects and bi-directional causality between response andtreatment by subsuming these effects within the GPS

9 / 16

Methodological contribution of the paper
ATE estimates are unbiased if the estimated GPS consistently
estimates the true GPS
A necessary condition is that X is sufficient to represent confounding
We show that with longitudinal data the GPS can be estimated via amixed model approach to address⋆ Unmeasured confounding: condition on unit level random effects,
or correlated random effects, to adjust for unobserved time-invariant
confounding: πb(d∗ |xit , ui ; αb)⋆ Reverse causality: condition on lagged values of the responsey, to allow for endogeneity fromyi,t−p , or the response history Hi,t−1y∗reverse causation: πb(d |xit , ui , Hi,t−1;αb)⋆ Dynamic assignment: include lagged values of the treatmentddi,t−p , or treatment history Hi,t−1, to represent the dynamic nature
y
d
∗
;α
b)
of assignment: πb(d |xit , ui , Hi,t−1 , Hi,t−1
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Algorithm for ATE estimation via mixed GPS model
1. Use a flexible mixed model (i.e. GAMM) to estimate fD|X (d|x, u; α)2. Use αb, with the appropriate density function, to calculate the GPSs:πb(d∗ |x, u; αb), for all d∗ of interest
3. Ensure common support by selecting only units which have areasonable probability of being treated across the range of dose
4. Estimate E(Y |D, πb(d|x, u; αb)) using a penalised spline model
5. Average over predicted values from 4., evaluated at at dose d∗ , toobtain a point estimate of the expected response at d∗ : µb(d∗ )
6. Repeat for all dose of interest, form the dose-response curve, andestimate ATEs:τb(d∗ ) = µb(d∗ ) − µb(0)7. Use a single (block) bootstrap re-sampling scheme over 1. to 6. toobtain standard errors

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Urban longitudinal data (TTI and MSA)
• Responses: annual proportional change in traffic volume (vmt), network
performance (delay per vmt), and productivity (average wage)• Treatment: annual proportional change in network lane miles (freeway
and arterial)• Pre-treatment covariates (confounders):⋆ Lagged responses: to capture reverse causality⋆ Congestion & traffic volume: measured by delay and vmt⋆ Network scale & mix: network length, mix of freeway / arterial⋆ Traffic mix: volume on freeway / arterial⋆ Mode characteristics: public transport patronage, state fuel price⋆ Economy: productivity, income and economic structure⋆ Employment and population distribution and growth• Unobserved confounders: physical characteristics, geographical features,
aspects of road network design, activity/travel behaviour patterns⋆ Random city-level effects specified in longitudinal mixed models• Models: Normal GAMMs for all sub models

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4
2
0−2

Average Treatment Effect (%)

6

Results: traffic volumes (vmt)

1

2

3

4

5

Capacity Expansion (%)

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Results: traffic volumes (vmt)

4
2
0
−2

Average Treatment Effect (%)

6⋆ evidence of induced
demand over the range ofdose having adjusted forconfounding

1

2

3

4

5

Capacity Expansion (%)

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Results: traffic volumes (vmt)

402⋆ ATE > proportional totreatment for doses ≤ 2−2

Average Treatment Effect (%)

6⋆ evidence of induced
demand over the range ofdose having adjusted forconfounding

1

2

3

4

5

Capacity Expansion (%)

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Results: traffic volumes (vmt)4⋆ ATE > proportional totreatment for doses ≤ 2

02⋆ on average 10% increasein lane miles → 9%increase in vmt net of‘natural growth’(estimated 1.4% p.a.)−2

Average Treatment Effect (%)

6⋆ evidence of induced
demand over the range ofdose having adjusted forconfounding

1

2

3

4

5

Capacity Expansion (%)

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Results: traffic volumes (vmt)4⋆ ATE > proportional totreatment for doses ≤ 2

02⋆ on average 10% increasein lane miles → 9%increase in vmt net of‘natural growth’(estimated 1.4% p.a.)−2

Average Treatment Effect (%)

6⋆ evidence of induced
demand over the range ofdose having adjusted forconfounding

1

2

3

4

5

⋆ capacity expansions in therange considered have notin general reduced traffic
density (vol. / cap.)

Capacity Expansion (%)

13 / 16

4
2
0−2

Average Treatment Effect (%)

6

Results: network performance (delay per vmt)

1

2

3

4

5

Capacity Expansion (%)

14 / 16

Results: network performance (delay per vmt)

4
2
0
−2

Average Treatment Effect (%)

6⋆ capacity expansions havenot ameliorated urban
congestion

1

2

3

4

5

Capacity Expansion (%)

14 / 16

Results: network performance (delay per vmt)

0

2

4

⋆ average road user has notexperienced reduced delay−2

Average Treatment Effect (%)

6⋆ capacity expansions havenot ameliorated urban
congestion

1

2

3

4

5

Capacity Expansion (%)

14 / 16

Results: network performance (delay per vmt)

4

⋆ average road user has notexperienced reduced delay

0

2⋆ aggregate cost ofcongestion has increased−2

Average Treatment Effect (%)

6⋆ capacity expansions havenot ameliorated urban
congestion

1

2

3

4

5

Capacity Expansion (%)

14 / 16

Results: network performance (delay per vmt)

4

⋆ average road user has notexperienced reduced delay2⋆ aggregate cost ofcongestion has increased0⋆ no statistically significant
effects on delay per vmt−2

Average Treatment Effect (%)

6⋆ capacity expansions havenot ameliorated urban
congestion

1

2

3

4

5

Capacity Expansion (%)

14 / 16

Results: network performance (delay per vmt)

4

⋆ average road user has notexperienced reduced delay2⋆ aggregate cost ofcongestion has increased⋆ no statistically significant
effects on delay per vmt

0⋆ this is the case even forlarge capacity expansions−2

Average Treatment Effect (%)

6⋆ capacity expansions havenot ameliorated urban
congestion

1

2

3

4

5

Capacity Expansion (%)

14 / 16

Results: network performance (delay per vmt)

4

⋆ average road user has notexperienced reduced delay2⋆ aggregate cost ofcongestion has increased⋆ no statistically significant
effects on delay per vmt

0⋆ this is the case even forlarge capacity expansions−2

Average Treatment Effect (%)

6⋆ capacity expansions havenot ameliorated urban
congestion

1

2

3

4

5

⋆ due to natural growthcongestion worsens
further (approx. 3% p.a.)Capacity Expansion (%)

14 / 16

1
0
−1
−2

Average Treatment Effect (%)

2

Results: productivity (average MSA wage)

1

2

3

4

5

Capacity Expansion (%)

15 / 16

Results: productivity (average MSA wage)

1
0
−1
−2

Average Treatment Effect (%)

2⋆ urban road network
expansions have notinduced higher
productivity

1

2

3

4

5

Capacity Expansion (%)

15 / 16

Results: productivity (average MSA wage)

1
−1

0

⋆ ‘naive’ regressions of
productivity on treatment
do indicate a +ve
association−2

Average Treatment Effect (%)

2⋆ urban road network
expansions have notinduced higher
productivity

1

2

3

4

5

Capacity Expansion (%)

15 / 16

Results: productivity (average MSA wage)

10⋆ ‘naive’ regressions of
productivity on treatment
do indicate a +ve
association−1⋆ but no significant ATEs
having isolated a viablesample and adjusted forconfounding−2

Average Treatment Effect (%)

2⋆ urban road network
expansions have notinduced higher
productivity

1

2

3

4

5

Capacity Expansion (%)

15 / 16

Results: productivity (average MSA wage)

10⋆ ‘naive’ regressions of
productivity on treatment
do indicate a +ve
association−1⋆ but no significant ATEs
having isolated a viablesample and adjusted forconfounding−2

Average Treatment Effect (%)

2⋆ urban road network
expansions have notinduced higher
productivity

1

2

3

4

5

⋆ no fall in interaction costs
and apparently no scaleeffects

Capacity Expansion (%)

15 / 16Case study 1: conclusions

16 / 16Case study 1: conclusions
Causal mixed model GPS approach provides a highly flexible framework
for ex-post evaluation of transport interventions

16 / 16Case study 1: conclusions
Causal mixed model GPS approach provides a highly flexible framework
for ex-post evaluation of transport interventions
Model indicates that urban road network expansions have induced
demand but have not ameliorated congestion or raised productivity

16 / 16Case study 1: conclusions
Causal mixed model GPS approach provides a highly flexible framework
for ex-post evaluation of transport interventions
Model indicates that urban road network expansions have induced
demand but have not ameliorated congestion or raised productivity
Results do not imply that there are no economic benefits from roadcapacity expansions per se:

16 / 16Case study 1: conclusions
Causal mixed model GPS approach provides a highly flexible framework
for ex-post evaluation of transport interventions
Model indicates that urban road network expansions have induced
demand but have not ameliorated congestion or raised productivity
Results do not imply that there are no economic benefits from roadcapacity expansions per se:⋆ results specific to marginal changes on mature congested urban networks

16 / 16Case study 1: conclusions
Causal mixed model GPS approach provides a highly flexible framework
for ex-post evaluation of transport interventions
Model indicates that urban road network expansions have induced
demand but have not ameliorated congestion or raised productivity
Results do not imply that there are no economic benefits from roadcapacity expansions per se:⋆ results specific to marginal changes on mature congested urban networks⋆ increased mobility with aggregate volume / capacity ratios constant

16 / 16Case study 1: conclusions
Causal mixed model GPS approach provides a highly flexible framework
for ex-post evaluation of transport interventions
Model indicates that urban road network expansions have induced
demand but have not ameliorated congestion or raised productivity
Results do not imply that there are no economic benefits from roadcapacity expansions per se:⋆ results specific to marginal changes on mature congested urban networks⋆ increased mobility with aggregate volume / capacity ratios constant⋆ network generalised costs do not improve and total urban delay rises

16 / 16Case study 1: conclusions
Causal mixed model GPS approach provides a highly flexible framework
for ex-post evaluation of transport interventions
Model indicates that urban road network expansions have induced
demand but have not ameliorated congestion or raised productivity
Results do not imply that there are no economic benefits from roadcapacity expansions per se:⋆ results specific to marginal changes on mature congested urban networks⋆ increased mobility with aggregate volume / capacity ratios constant⋆ network generalised costs do not improve and total urban delay rises⋆ the scale (increased traffic) effect does not appear to influence
productivity (either +ve or -ve)

16 / 16Case study 1: conclusions
Causal mixed model GPS approach provides a highly flexible framework
for ex-post evaluation of transport interventions
Model indicates that urban road network expansions have induced
demand but have not ameliorated congestion or raised productivity
Results do not imply that there are no economic benefits from roadcapacity expansions per se:⋆ results specific to marginal changes on mature congested urban networks⋆ increased mobility with aggregate volume / capacity ratios constant⋆ network generalised costs do not improve and total urban delay rises⋆ the scale (increased traffic) effect does not appear to influence
productivity (either +ve or -ve)

To improve urban road network performance and raise productivity a
combination of efficient pricing with investment in both roads and masstransit may be more effective

16 / 16

The problem of confounding
X = city
characteristics

Y = productivity

D = capacity
expansion

The relationship between capacity and productivity is confounded
by a set of city characteristics which⋆ Are important for productivity⋆ Influence the level of capacity expansion received

return16 / 16