Testing the Unconfoundedness Assumption via Inverse Probability Weighted Estimators of (L)ATT

Donald, Stephanie Hemelryk; Hsu, Yu Chin; Lieli, Robert P.

doi:10.1080/07350015.2014.888290

Cited by 52 publications

(42 citation statements)

References 25 publications

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“…(), Abadie (, 2003), Frölich () and Donald et al. (, ), the LATE is defined as

LATE false(x_{s} false) \equiv E false[Y (1) - Y (0) false| X_{s} = x_{s}, scriptC false]

, where

scriptC

denotes the group of compliers and the null hypothesis of interest is defined as

H_{0}^{italiclate} : LATE false(x_{s} false) \geq 0,

for all

x_{s} \in {scriptX}_{s}

. It is well known that

LATE (x_{s})

is identified by

LATE false(x false) = E ([] \frac{Z Y}{q (X)} - \frac{(1 - Z) Y}{1 - q (X)} | X_{s} = x_{s}) / E ([] \frac{Z D}{q (X)} - \frac{(1 - Z) D}{1 - q (X)} | X_{s} = x_{s}),

where

q (x) = P (Z = 1 | X = x)

.…”

Section: Extensionsmentioning

confidence: 99%

“…Second, we extend our test for the null hypothesis that the conditional stochastic dominance relation between the potential outcomes holds for all values of the subset of the covariates. Last, we extend our test to cases where the treatment assignment is endogenous, as in the local average treatment effect set-up of Imbens and Angrist (1994), Abadie et al (2002), Abadie (2002Abadie ( , 2003, Frölich (2007) and Donald et al (2014aDonald et al ( , 2014b.…”

Section: Introductionmentioning

confidence: 99%

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Consistent tests for conditional treatment effects

Hsu

2017

The Econometrics Journal

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Summary We construct tests for the null hypothesis that the conditional average treatment effect is non‐negative, conditional on every possible value of a subset of covariates. Testing such a null hypothesis can provide more information than the sign of the average treatment effects parameter. The null hypothesis can be characterized as infinitely many of unconditional moment inequalities. A Kolmogorov–Smirnov test is constructed based on these unconditional moment inequalities, and a simulated critical value is proposed. It is shown that our test can control the size uniformly over a broad set of data‐generating processes asymptotically, that it is consistent against fixed alternatives and that it is unbiased against some N−1/2 local alternatives. Several extensions of our test are also considered and we apply our tests to examine the effect of a job‐training programme on real earnings.

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“…(), Abadie (, 2003), Frölich () and Donald et al. (, ), the LATE is defined as

LATE false(x_{s} false) \equiv E false[Y (1) - Y (0) false| X_{s} = x_{s}, scriptC false]

, where

scriptC

denotes the group of compliers and the null hypothesis of interest is defined as

H_{0}^{italiclate} : LATE false(x_{s} false) \geq 0,

for all

x_{s} \in {scriptX}_{s}

. It is well known that

LATE (x_{s})

is identified by

LATE false(x false) = E ([] \frac{Z Y}{q (X)} - \frac{(1 - Z) Y}{1 - q (X)} | X_{s} = x_{s}) / E ([] \frac{Z D}{q (X)} - \frac{(1 - Z) D}{1 - q (X)} | X_{s} = x_{s}),

where

q (x) = P (Z = 1 | X = x)

.…”

Section: Extensionsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Consistent tests for conditional treatment effects

Hsu

2017

The Econometrics Journal

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“…We impose this assumption for the sake of ease of discussion and note that our results could be easily extended to cases including discrete covariates and mediators, however, at the cost of more burdensome notation. That is, the sample would need to be stratified on the discrete values and SLE would have to be performed separately for each stratum, see Donald, Hsu, and Lieli (2014).…”

Section: Asymptotic Behaviourmentioning

confidence: 99%

Nonparametric estimation of natural direct and indirect effects based on inverse probability weighting

Hsu

Huber

Lai

2018

Journal of Econometric Methods

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Using a sequential conditional independence assumption, this paper discusses fully nonparametric estimation of natural direct and indirect causal effects in causal mediation analysis based on inverse probability weighting. We propose estimators of the average indirect effect of a binary treatment, which operates through intermediate variables (or mediators) on the causal path between the treatment and the outcome, as well as the unmediated direct effect. In a first step, treatment propensity scores given the mediator and observed covariates or given covariates alone are estimated by nonparametric series logit estimation. In a second step, they are used to reweigh observations in order to estimate the effects of interest. We establish root-n consistency and asymptotic normality of this approach as well as a weighted version thereof. The latter allows evaluating effects on specific subgroups like the treated, for which we derive the asymptotic properties under estimated propensity scores. We also provide a simulation study and an application to an information intervention about male circumcisions.

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“…When a causal effect is identified, a test of the unconfoundedness assumption may be devised by comparing the estimates of the causal effects obtained both under the unconfoundedness assumption and using the instrument (classical Durbin-WuHausman (DWH) test in a parametric setting). This was recently used by Donald et al [11] to propose a test of the unconfoundedness assumption in a nonparametric framework.…”

Section: Introductionmentioning

confidence: 99%

“…Indeed, to obtain the nonparametric identification of a local average causal effects stronger (and untestable) assumptions must be made on the instrument, see, e.g. Imbens and Angrist [12], Angrist et al [4], Angrist and Fernandez-Val [13], Donald et al [11] and Guo et al [14]. In particular, these papers use a monotonicity assumption saying that the instrument must affect the treatment in a monotone fashion, as well as do not allow for unobserved heterogeneity to affect both the instrument and the treatment.…”

Section: Introductionmentioning

confidence: 99%

Testing for the Unconfoundedness Assumption Using an Instrumental Assumption

Luna

Johansson²

2014

Journal of Causal Inference

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The identification of average causal effects of a treatment in observational studies is typically based either on the unconfoundedness assumption (exogeneity of the treatment) or on the availability of an instrument. When available, instruments may also be used to test for the unconfoundedness assumption. In this paper, we present a set of assumptions on an instrumental variable which allows us to test for the unconfoundedness assumption, although they do not necessarily yield nonparametric identification of an average causal effect. We propose a test for the unconfoundedness assumption based on the instrumental assumptions introduced and give conditions under which the test has power. We perform a simulation study and apply the results to a case study where the interest lies in evaluating the effect of job practice on employment.

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Testing the Unconfoundedness Assumption via Inverse Probability Weighted Estimators of (L)ATT

Cited by 52 publications

References 25 publications

Consistent tests for conditional treatment effects

Consistent tests for conditional treatment effects

Nonparametric estimation of natural direct and indirect effects based on inverse probability weighting

Testing for the Unconfoundedness Assumption Using an Instrumental Assumption

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