Statistical models and causal inference: a dialogue with the social sciences
"David A. Freedman presents here a definitive synthesis of his approach to causal inference in the social sciences. He explores the foundations and limitations of statistical modeling, illustrating basic arguments with examples from political science, public policy, law, and epidemiology. Freed...
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Cambridge [u.a.]
Cambridge Univ. Press
2010
|
| Ausgabe: | 1. publ. |
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Zusammenfassung: | "David A. Freedman presents here a definitive synthesis of his approach to causal inference in the social sciences. He explores the foundations and limitations of statistical modeling, illustrating basic arguments with examples from political science, public policy, law, and epidemiology. Freedman maintains that many new technical approaches to statistical modeling constitute not progress, but regress. Instead, he advocates a 'shoe leather' methodology, which exploits natural variation to mitigate confounding and relies on intimate knowledge of the subject matter to develop meticulous research designs and eliminate rival explanations. When Freedman first enunciated this position, he was met with scepticism, in part because it was hard to believe that a mathematical statistician of his stature would favor 'low-tech' approaches. But the tide is turning. Many social scientists now agree that statistical technique cannot substitute for good research design and subject matter knowledge. This book offers an integrated presentation of Freedman's views"--Provided by publisher. |
| Beschreibung: | XVI, 399 S. graph. Darst. |
| ISBN: | 9780521123907 9780521195003 |
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Datensatz im Suchindex
| _version_ | 1804140008714534912 |
|---|---|
| adam_text | Contents
Preface
x¡
Editors
Introduction:
Inference and Shoe Leather
x¡¡¡
Parti
Statistical Modeling: Foundations and Limitations
1.
Issues in the Foundations of Statistics:
Probability and Statistical Models
з
Bayesians and frequentists disagree on the meaning of probability
and other foundational issues, but both schools face the problem of model
validation. Statistical models have been used successfully in the physical
and life sciences. However, they have not advanced the study of social
phenomena. How do models connect with reality? When are they likely
to deepen understanding? When are they likely to be sterile or misleading?
2.
Statistical Assumptions as Empirical Commitments
23
Statistical inference with convenience samples is risky. Real progress
depends on a deep understanding of how the data were generated. No
amount of statistical maneuvering will get very far without recognizing
that statistical issues and substantive issues overlap.
3.
Statistical Models and Shoe Leather
45
Regression models are used to make causal arguments in a wide vari¬
ety of applications, and it is time to evaluate the results. Snow s work on
cholera is a success story for causal inference based on nonexperimental
data, which was collected through great expenditure of effort and shoe
leather. Failures are also discussed. Statistical technique is seldom an ad¬
equate substitute for substantive knowledge of the topic, good research
design, relevant data, and empirical tests in diverse settings.
vj Contents
Part II
Studies in Political Science, Public Policy,
and Epidemiology
4.
Methods for Census
2000
and Statistical Adjustments
65
The U.S. Census is a sophisticated, complex undertaking, carried out
on a vast scale. It is remarkably accurate. Statistical adjustments are likely
to introduce more error than they remove. This issue was litigated all the
way to the Supreme Court, which in
1999
unanimously supported the
Secretary of Commerce s decision not to adjust the
2000
Census.
5.
On Solutions to the Ecological Inference Problem
83
Gary King s book, A Solution to the Ecological Inference Problem,
claims to offer realistic estimates of the uncertainty of ecological esti¬
mates. Applying King s method and three of his main diagnostics to data
sets where the truth is known shows that his diagnostics cannot distinguish
between cases where estimates are accurate and those where estimates are
far off the mark. King s claim to have arrived at a solution to this problem
is premature.
6.
Rejoinder to King
97
King s method works with some data sets but not others. As a theo¬
retical matter, inferring the behavior of subgroups from aggregate data is
generally impossible: The relevant parameters are not identifiable. King s
diagnostics do not discriminate between probable successes and probable
failures.
7.
Black Ravens, White Shoes, and Case Selection:
Inference with Categorical Variables
105
Statistical ideas can clarify issues in qualitative analysis such as case
selection. In political science, an important argument about case selection
evokes Hempel s Paradox of the Ravens. This paradox can be resolved by
distinguishing between population and sample inferences.
8.
What is the Chance of an Earthquake? H5
Making sense of earthquake forecasts is surprisingly difficult. In part,
this is because the forecasts are based on a complicated mixture of geo¬
logical maps, rales of thumb, expert opinion, physical models, stochastic
models, and numerical simulations, as well as geodetic, seismic, and pa-
leoseismic data. Even the concept of probability is hard to define in this
Contents
vii
context. Other models of risk for emergency preparedness, as well as
models of economic risk, face similar difficulties.
9.
Salt and Blood Pressure:
Conventional Wisdom Reconsidered
ізі
Experimental evidence suggests that the effect of a large reduction
in salt intake on blood pressure is modest and that health consequences
remain to be determined. Funding agencies and medical journals have
taken a stronger position favoring the salt hypothesis than is warranted,
demonstrating how misleading scientific findings can influence public
policy.
10.
The Swine Flu Vaccine and
Guillain-Barré
Syndrome:
A Case Study in Relative Risk and Specific Causation
151
Epidemiologie
methods were developed to prove general causation:
identifying exposures that increase the risk of particular diseases. Courts
of law often are more interested in specific causation: On balance of prob¬
abilities, was the plaintiff s disease caused by exposure to the agent in
question? There is a considerable gap between relative risks and proof of
specific causation because individual differences affect the interpretation
of relative risk for a given person. This makes specific causation especially
hard to establish.
11.
Survival Analysis: An Epidemiological Hazard?
169
Proportional-hazards models are frequently used to analyze data from
randomized controlled trials. This is a mistake. Randomization does not
justify the models, which are rarely informative. Simpler methods work
better. This discussion matters because survival analysis has introduced a
new hazard: It can lead to serious mistakes in medical treatment. Survival
analysis is, unfortunately, thriving in other disciplines as well.
Part III
New Developments: Progress or Regress?
12.
On Regression Adjustments in Experiments
with Several Treatments
195
Regression adjustments are often made to experimental data to ad¬
dress confounders that may not be balanced by randomization. Since
randomization does not justify the models, bias is likely. Neither are the
usual variance calculations to be trusted. Neyman s non-parametric model
viii
Contents
serves to evaluate regression adjustments. A bias term is isolated, and con¬
ditions are given for unbiased estimation in finite samples.
13.
Randomization Does Not Justify Logistic Regression
219
The logit model is often used to analyze experimental data. Theory
and simulation show that randomization does not justify the model, so
the usual estimators can be inconsistent. Neyman s non-parametric setup
is used as a benchmark: Each subject has two potential responses, one if
treated and the other if untreated; only one of the two responses can be
observed. A consistent estimator is proposed.
14.
The Grand Leap
243
A number of algorithms purport to discover causal structure from
empirical data with no need for specific subject-matter knowledge. Ad¬
vocates have no real success stories to report. These algorithms solve
problems quite removed from the challenge of causal inference from im¬
perfect data. Nor do they resolve long-standing philosophical questions
about the meaning of causation.
15.
On Specifying Graphical Models for Causation,
and the Identification Problem
255
Causal relationships cannot be inferred from data by fitting graphical
models without prior substantive knowledge of how the data were gen¬
erated. Successful applications are rare because few causal pathways can
be excluded a priori.
16.
Weighting Regressions by Propensity Scores
279
The use of propensity scores to reduce bias in regression analysis is
increasingly common in the social sciences. Yet weighting is likely to in¬
crease random error in the estimates and to bias the estimated standard er¬
rors downward, even when selection mechanisms are well understood. If
investigators have a good causal model, it seems better just to fit the model
without weights. If the causal model is improperly specified, weighting is
unlikely to help.
17.
On the So-Called Huber Sandwich Estimator
and Robust Standard Errors
295
In applications where the statistical model is nearly correct, the Huber
Sandwich Estimator makes little difference. On the other hand, if the
model is seriously in error, the parameters being estimated are likely to
be meaningless, except perhaps as descriptive statistics.
Contents
ix
18.
Endogeneity in
Probit
Response Models
зоб
The usual
Heekman
two-step procedure should not be used for remov¬
ing endogeneity bias in
probit
regression. From a theoretical perspective
this procedure is unsatisfactory, and likelihood methods are superior. Un¬
fortunately, standard software packages do a poor job of maximizing the
biprobit likelihood function, even if the number of covariates is small.
19.
Diagnostics Cannot Have Much Power
Against General Alternatives
323
Model diagnostics cannot have much power against omnibus alter¬
natives. For instance, the hypothesis that observations are independent
cannot be tested against the general alternative that they are dependent
with power that exceeds the level of the test. Thus, the basic assumptions
of regression cannot be validated from data.
Part IV
Shoe Leather Revisited
20.
On Types of Scientific Inquiry:
The Role of Qualitative Reasoning
337
Causal inference can be strengthened in fields ranging from epidemi¬
ology to political science by linking statistical analysis to qualitative
knowledge. Examples from epidemiology show that substantial progress
can derive from informal reasoning, qualitative insights, and the creation
of novel data sets that require deep substantive understanding and a great
expenditure of effort and shoe leather. Scientific progress depends on re¬
futing conventional ideas if they are wrong, developing new ideas that
are better, and testing the new ideas as well as the old ones. Qualitative
evidence can play a key role in all three tasks.
References and Further Reading
357
Index
393
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| spelling | Freedman, David 1938-2008 Verfasser (DE-588)132210819 aut Statistical models and causal inference a dialogue with the social sciences David Freedman ; ed. by David Collier ... 1. publ. Cambridge [u.a.] Cambridge Univ. Press 2010 XVI, 399 S. graph. Darst. txt rdacontent n rdamedia nc rdacarrier "David A. Freedman presents here a definitive synthesis of his approach to causal inference in the social sciences. He explores the foundations and limitations of statistical modeling, illustrating basic arguments with examples from political science, public policy, law, and epidemiology. Freedman maintains that many new technical approaches to statistical modeling constitute not progress, but regress. Instead, he advocates a 'shoe leather' methodology, which exploits natural variation to mitigate confounding and relies on intimate knowledge of the subject matter to develop meticulous research designs and eliminate rival explanations. When Freedman first enunciated this position, he was met with scepticism, in part because it was hard to believe that a mathematical statistician of his stature would favor 'low-tech' approaches. But the tide is turning. Many social scientists now agree that statistical technique cannot substitute for good research design and subject matter knowledge. This book offers an integrated presentation of Freedman's views"--Provided by publisher. Sozialwissenschaften Causation Linear models (Statistics) Social sciences Statistical methods Empirische Forschung (DE-588)4300400-3 gnd rswk-swf Kausalität (DE-588)4030102-3 gnd rswk-swf Statistik (DE-588)4056995-0 gnd rswk-swf Sozialwissenschaften (DE-588)4055916-6 gnd rswk-swf Sozialwissenschaften (DE-588)4055916-6 s Empirische Forschung (DE-588)4300400-3 s Statistik (DE-588)4056995-0 s Kausalität (DE-588)4030102-3 s 1\p DE-604 Collier, David Sonstige oth Digitalisierung UB Bayreuth application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=018006136&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis 1\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk |
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| title | Statistical models and causal inference a dialogue with the social sciences |
| title_auth | Statistical models and causal inference a dialogue with the social sciences |
| title_exact_search | Statistical models and causal inference a dialogue with the social sciences |
| title_full | Statistical models and causal inference a dialogue with the social sciences David Freedman ; ed. by David Collier ... |
| title_fullStr | Statistical models and causal inference a dialogue with the social sciences David Freedman ; ed. by David Collier ... |
| title_full_unstemmed | Statistical models and causal inference a dialogue with the social sciences David Freedman ; ed. by David Collier ... |
| title_short | Statistical models and causal inference |
| title_sort | statistical models and causal inference a dialogue with the social sciences |
| title_sub | a dialogue with the social sciences |
| topic | Sozialwissenschaften Causation Linear models (Statistics) Social sciences Statistical methods Empirische Forschung (DE-588)4300400-3 gnd Kausalität (DE-588)4030102-3 gnd Statistik (DE-588)4056995-0 gnd Sozialwissenschaften (DE-588)4055916-6 gnd |
| topic_facet | Sozialwissenschaften Causation Linear models (Statistics) Social sciences Statistical methods Empirische Forschung Kausalität Statistik |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=018006136&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT freedmandavid statisticalmodelsandcausalinferenceadialoguewiththesocialsciences AT collierdavid statisticalmodelsandcausalinferenceadialoguewiththesocialsciences |